Understanding: How AI Agents Understand User Inputs

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rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless text-wrap rm-Sidebar-link" target="_self" href="/docs/welcome"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Welcome to the docs</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless text-wrap rm-Sidebar-link" target="_self" href="/docs/voiceflow-in-your-stack"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">How Voiceflow fits in your tech stack</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/step-1"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Quick start guide" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW 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class="Sidebar-link-text_label1gCT_uPnx7Gu">Step 3: Deploy your agent</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/building-a-telecom-customer-support-agents-to-troubleshoot-complex-questions"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Use cases" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Use cases</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/building-a-telecom-customer-support-agents-to-troubleshoot-complex-questions"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Building a telecom customer support agent</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/removing-hallucinations-for-a-banking-customer-support-bot"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Removing hallucinations for a banking customer support agent</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/a-nyt-cooking-recipe-agent-using-rag"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">A NYT Cooking recipe agent using RAG</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/rebuilding-a-retail-stores-digital-agent"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Rebuilding Sephora's digital agent</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/managing-workspaces-teams"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Admin and support" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Admin and support</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/managing-workspaces-teams"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Managing workspaces and team members</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/sso"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Single Sign-On (SSO) and SAML support</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/roles-permissions"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Roles and permissions</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/billing-plans"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Billing and plans</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/token-top-up"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Token Top-up</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/get-help"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Get help</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/agent-settings"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Important Agent Settings</span></span></a></li></ul></li></ul></section><section class="Sidebar-listWrapper6Q9_yUrG906C rm-Sidebar-section"><h2 class="Sidebar-headingTRQyOa2pk0gh rm-Sidebar-heading">Courses</h2><ul class="Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/voiceflow-basics"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Voiceflow Basics" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Voiceflow Basics</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/voiceflow-basics"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Part 1: Get Setup</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/voiceflow-basics-workflows"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Part 2: Get Familiar with Workflows</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/voiceflow-basics-user-input"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Part 3: Accept User Input</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/part-1-advanced-faq"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Build an AI Support Agent" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Build an AI Support Agent</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/part-1-advanced-faq"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Part 1: Answer Questions</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/part-2-sending-a-ticket-to-zendesk"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Part 2: Sending a Ticket to Zendesk</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/part-3-book-a-demo"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Part 3: Book a Demo</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/part-4-debugging"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Part 4: Deploy & Customize Webchat</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/guide-qna-chatbot-overview"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Plan, Build, and Launch a Production Q&A Slack Agent" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Plan, Build, and Launch a Production Q&A Slack Agent</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/guide-qna-chatbot-overview"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Overview</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/guide-qna-chatbot-planning"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Planning</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/guide-qna-chatbot-building"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Building</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/guide-qna-chatbot-deploying"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">QA and Deploying</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/guide-qna-chatbot-technical-walkthrough"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Full Technical Walkthrough</span></span></a></li></ul></li></ul></section><section class="Sidebar-listWrapper6Q9_yUrG906C rm-Sidebar-section"><h2 class="Sidebar-headingTRQyOa2pk0gh rm-Sidebar-heading">Building Agents</h2><ul class="Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless text-wrap rm-Sidebar-link" target="_self" href="/docs/building-overview"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Overview</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/workspaces-agents"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Platform fundamentals" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Platform fundamentals</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/workspaces-agents"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Workspaces and Agents</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/cms"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Content Management System (CMS)</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/canvas"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Canvas</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/steps-blocks"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Steps and Blocks</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/workflows"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Workflows</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/components"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Components</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/template-library"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Template Library</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/test-tool"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Test Tool</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/teams"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Collaboration features</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/sharing-prototypes"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Sharing Prototypes</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/publishing-environments-backups"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Publishing, Environments and Backups</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/languages"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Languages</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/importing-exporting"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Importing or exporting projects and functions</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/secrets-manager"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Secrets Manager</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/listen-fundamentals"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Understand" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Understand</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a aria-current="page" class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link active" target="_self" href="/docs/listen-fundamentals"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Understanding: How AI Agents Understand User Inputs</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/intents"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Intents" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Intents</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/intents"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Introduction to Intents</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/using-the-intent-cms"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Using the Intent CMS</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/llm-intent-classification-method"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Intent Classification using LLMs (Hybrid)</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/nlu-classification-method"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Intent Classification using NLU</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/previewing-intent-classification-and-improvements"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Previewing Intent Classification and Improvements</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/introduction-to-entities"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Entities" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Entities</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/introduction-to-entities"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Introduction to Entities</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/using-the-entity-cms"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Using the Entities CMS</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/introduction-to-events"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Events" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Events</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/introduction-to-events"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Introduction to Events</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/using-the-events-cms"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Using Events</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/trigger"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Steps & Tools" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Steps & Tools</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/trigger"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Trigger</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/choice-v2"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Choice</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/choice"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Choice (legacy)</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/capture-v2"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Capture</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/capture"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Capture (legacy)</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/buttons-v2"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Buttons</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/memory"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Memory</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/understanding-heirarchy"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Improving understanding</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/decide-overview"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Decide" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Decide</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/decide-overview"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Overview</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/variables-set"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Set</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/set-ai"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Set AI step</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/logic"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Conditions</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/actions"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Actions</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/api-step"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">API step</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/javascript-step"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">JavaScript step</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/custom-functions"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Functions" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Functions</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/custom-functions"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Introduction to developing functions</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/developing-functions"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Implementing function code</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/functions-listen"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Supporting listen in functions</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/function-step"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Function step</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/kb-search"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">KB Search</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/respond-overview"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Respond" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Respond</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/respond-overview"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Overview</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/message"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Messages</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/images"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Images</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/cards-carousels"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Cards and Carousels</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/speak"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Speak</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/response-ai"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Response AI step</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/knowledge-base"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Knowledge Base</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/advanced-knowledge-base"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Advanced Knowledge Base</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/custom-actions"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Custom Actions</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/no-match"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">No Match handling</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/no-reply-response"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">No Reply handling</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/prompt-step"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Prompt</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/general-best-practices"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Best Practices" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Best Practices</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/general-best-practices"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Getting started</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/knowledge-base-best-practices"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Knowledge Base</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/workflow-builder"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Workflow Builder</span></span></a></li></ul></li></ul></section><section class="Sidebar-listWrapper6Q9_yUrG906C rm-Sidebar-section"><h2 class="Sidebar-headingTRQyOa2pk0gh rm-Sidebar-heading">Deploying Agents</h2><ul class="Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless text-wrap rm-Sidebar-link" target="_self" href="/docs/deploying-overview"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Overview</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/web-chat-overview"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Web Chat" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Web Chat</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/web-chat-overview"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Overview</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/custom-web-chat-widgets"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Custom forms and extensions</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/customization-configuration"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Adding custom Variables</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/embed-customize-styling"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Embed and customize styling</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/custom-configurations"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Modify configurations</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/web-chat-api"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Custom Triggers</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/react-chat"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Open sourced react-chat</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/setting-up-twilio-integration"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Voice (Phone Number)" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Voice (Phone Number)</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/setting-up-twilio-integration"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Setting up Twilio integration</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/configuring-voice-settings"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Configuring voice settings</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/advanced-using-custom-voice-actions"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Advanced - Using custom voice actions</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/examples"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Examples</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/text-deployment-overview"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Text-based interfaces" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Text-based interfaces</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/text-deployment-overview"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Overview</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/deploy-whatsapp"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">WhatsApp</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/deploy-discord"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Discord</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/deploy-slack"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Slack</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/deploy-telegram"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Telegram</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/deploy-twilio-sms"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Twilio SMS</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/deploy-ms-teams"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Microsoft Teams</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/voice-deployment-overview"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Voice-based interfaces" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Voice-based interfaces</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/voice-deployment-overview"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Overview</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/deploy-vapi"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">VAPI</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" rel="noopener" target="_blank" data-active="" href="https://xavidop.me/alexa/2023-11-21-voiceflow-alexa/"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Alexa</span></span><i aria-hidden="true" class="Sidebar-link-externalIcon1RcnjKm-3Ft8 icon-arrow-up-right"></i></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/deploy-twilio-voice"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Twilio Voice IVR</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/elevenlabs-api-integration"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Custom TTS (ElevenLabs)</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/deploy-whisper-asr"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Custom ASR (Whisper)</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/custom-interfaces-overview"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Custom interfaces and deploying through APIs" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Custom interfaces and deploying through APIs</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/custom-interfaces-overview"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Overview</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/custom-channel-api"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Custom deployment channel (API)</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/custom-interface-unity"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Unity AI Powered NPC</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/custom-interface-shopify"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Shopify In-Store Copilot</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/react-embedded-educhat"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Documentation Copilot</span></span></a></li></ul></li></ul></section><section class="Sidebar-listWrapper6Q9_yUrG906C rm-Sidebar-section"><h2 class="Sidebar-headingTRQyOa2pk0gh rm-Sidebar-heading">Improving Agents</h2><ul class="Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/custom-llm"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Agent enhancement cookbook" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Agent enhancement cookbook</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/custom-llm"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Bring your own LLM</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/langchain-knowledge-base"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Custom Knowledge Base</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/live-chat-handoff"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Live Chat Handoff</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/building-advanced-analytics"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Building Advanced Analytics</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/advanced-rag-filtering-and-tagging"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Advanced RAG Filtering and Tagging</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/adding-live-internet-search-to-your-rag-solution"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Adding live internet search to your RAG solution</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/pii-redaction-for-ai-agents"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">PII redaction for AI Agents</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/llmlingua2-prompt-compression"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">LLMLingua2 Prompt Compression</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/gpt4o-fallback-claude3-aigateway"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">GPT-4o with fallback to Claude 3 Sonnet</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/integrating-perplexity-into-voiceflow"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Integrating Perplexity into Voiceflow</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/automated-emails-with-user-transcripts"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Generate Automated Emails with User Transcripts</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/using-voiceflow-with-open-source-models"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Using Voiceflow with Open Source Models</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/custom-voices-for-your-agent-using-elevenlabs"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Custom Voices for your Agent using ElevenLabs</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless text-wrap rm-Sidebar-link" target="_self" href="/docs/transcripts"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Transcripts</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless text-wrap rm-Sidebar-link" target="_self" href="/docs/analytics"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Analytics</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless text-wrap rm-Sidebar-link" target="_self" href="/docs/how-does-the-knowledge-base-work"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">How does the Knowledge Base work</span></span></a></li></ul></section><section class="Sidebar-listWrapper6Q9_yUrG906C rm-Sidebar-section"><h2 class="Sidebar-headingTRQyOa2pk0gh rm-Sidebar-heading">Prompting and LLMs</h2><ul class="Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/prompts-cms-and-editor"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Prompts" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Prompts</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/prompts-cms-and-editor"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Prompts CMS</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless text-wrap rm-Sidebar-link" target="_self" href="/docs/using-large-language-models-llms-in-voiceflow"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">What are LLMs?</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/prompting"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Prompting" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Prompting</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/types-of-prompts"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Types of prompts</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/crafting-great-prompts"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Crafting great prompts</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/hallucination-prevention"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for Hallucinations" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Hallucinations</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/hallucination-reduction"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Hallucination reduction</span></span></a></li></ul></li></ul></section><section class="Sidebar-listWrapper6Q9_yUrG906C rm-Sidebar-section"><h2 class="Sidebar-headingTRQyOa2pk0gh rm-Sidebar-heading">RESOURCES</h2><ul class="Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 Sidebar-link_parent3OBrzjAJRSfq text-wrap rm-Sidebar-link" target="_self" href="/docs/what-are-tokens"><span class="Sidebar-link-textLuTE1ySm4Kqn"><button aria-expanded="false" aria-label="Show subpages for FAQ" class="Sidebar-link-buttonWrapper3hnFHNku8_BJ"><i class="Sidebar-link-expandIcon2yVH6SarI6NW icon-chevron-rightward"></i></button><span class="Sidebar-link-text_label1gCT_uPnx7Gu">FAQ</span></span></a><ul class="subpages Sidebar-list3cZWQLaBf9k8 rm-Sidebar-list"><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/what-are-tokens"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">What are Tokens?</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/how-to-find-the-sitemap-for-a-website"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">How to find the Sitemap for a website</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/ai-token-purchase-confirmation"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">AI Token Purchase Confirmation</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/how-to-find-your-assistant-version-id-and-project-id"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">How to find your Assistant version ID and project ID</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/how-to-fix-a-loading-screen-error"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">How to Fix a Loading Screen Error</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/how-to-use-speech-effects"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">How to use speech effects?</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/missing-steps"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">I'm missing steps?</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/how-voiceflows-nlu-exports-work"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">How Voiceflow's NLU Exports Work</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/adjusting-the-nlu-intent-confidence-threshold"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Adjusting the NLU Intent Confidence Threshold</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/how-do-i-log-out-of-voiceflow"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">How do I log out of Voiceflow?</span></span></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/using-specific-models-in-voiceflow-kb-query-api"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Using Specific Models in Voiceflow KB Query API</span></span></a></li></ul></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless text-wrap rm-Sidebar-link" rel="noopener" target="_blank" data-active="" href="https://link.voiceflow.com/community"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Join our Discord Community</span></span><i aria-hidden="true" class="Sidebar-link-externalIcon1RcnjKm-3Ft8 icon-arrow-up-right"></i></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless text-wrap rm-Sidebar-link" rel="noopener" target="_blank" data-active="" href="https://www.voiceflow.com/security"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Security at Voiceflow</span></span><i aria-hidden="true" class="Sidebar-link-externalIcon1RcnjKm-3Ft8 icon-arrow-up-right"></i></a></li><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless text-wrap rm-Sidebar-link" rel="noopener" target="_blank" data-active="" href="https://www.voiceflow.com/templates"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Templates</span></span><i aria-hidden="true" class="Sidebar-link-externalIcon1RcnjKm-3Ft8 icon-arrow-up-right"></i></a></li></ul></section></div></nav><article class="rm-Article " id="content"><header id="content-head"><div class="row clearfix"><div class="col-xs-9"><h1>Understanding: How AI Agents Understand User Inputs</h1></div><div class="col-xs-3"><a class="suggestEdits" rel="nofollow" href="/edit/listen-fundamentals"><i aria-hidden="true" class="icon icon-edit"></i> Suggest Edits</a></div></div></header><div class="grid-container-fluid" id="content-container"><section class="content-body grid-75"><div dehydrated="<h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="introduction"></div><div class="heading-text"><div id="section-introduction" class="heading-anchor_backwardsCompatibility"></div>Introduction</div><a aria-label="Skip link to Introduction" class="heading-anchor-icon fa fa-anchor" href="#introduction"></a></h2> <p>In the rapidly evolving field of conversational AI, one of the most critical aspects is enabling AI agents to <strong>understand user inputs</strong> effectively. Whether it&#x27;s a simple greeting, a complex question, or selecting a button in a user interface, understanding the user&#x27;s intent is paramount for creating meaningful and efficient interactions.</p> <p>This guide provides a high-level overview of how AI agents interpret and process user inputs, especially natural language, to deliver appropriate responses. We&#x27;ll explore the fundamental concepts that empower AI agents to comprehend user queries, including <strong>Intents</strong>, <strong>Natural Language Understanding (NLU)</strong>, <strong>Large Language Models (LLMs)</strong>, <strong>Memory and Context</strong>, and various input methods like button selections.</p> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="what-does-it-mean-for-an-ai-agent-to-understand-a-users-query"></div><div class="heading-text"><div id="section-what-does-it-mean-for-an-ai-agent-to-understand-a-users-query" class="heading-anchor_backwardsCompatibility"></div>What Does It Mean for an AI Agent to Understand a User&#x27;s Query?</div><a aria-label="Skip link to What Does It Mean for an AI Agent to Understand a User&#x27;s Query?" class="heading-anchor-icon fa fa-anchor" href="#what-does-it-mean-for-an-ai-agent-to-understand-a-users-query"></a></h2> <p>Understanding a user&#x27;s query involves several layers of processing:</p> <ol> <li><strong>Capturing the Input</strong>: Receiving the user&#x27;s message, which could be text, voice, or a selection.</li> <li><strong>Interpreting the Input</strong>: Analyzing the input to determine what the user wants or needs.</li> <li><strong>Determining the Intent</strong>: Identifying the purpose behind the user&#x27;s message.</li> <li><strong>Extracting Relevant Information</strong>: Pulling out specific details or entities that are important for the response.</li> <li><strong>Generating an Appropriate Response</strong>: Crafting a reply that addresses the user&#x27;s intent accurately and helpfully.</li> </ol> <p>An AI agent&#x27;s ability to perform these steps effectively defines its <strong>understanding</strong> of the user. This process relies on various technologies and methodologies, which we&#x27;ll explore in the following sections.</p> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="natural-language-understanding-nlu"></div><div class="heading-text"><div id="section-natural-language-understanding-nlu" class="heading-anchor_backwardsCompatibility"></div>Natural Language Understanding (NLU)</div><a aria-label="Skip link to Natural Language Understanding (NLU)" class="heading-anchor-icon fa fa-anchor" href="#natural-language-understanding-nlu"></a></h2> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="what-is-nlu"></div><div class="heading-text"><div id="section-what-is-nlu" class="heading-anchor_backwardsCompatibility"></div>What is NLU?</div><a aria-label="Skip link to What is NLU?" class="heading-anchor-icon fa fa-anchor" href="#what-is-nlu"></a></h3> <p><strong>Natural Language Understanding (NLU)</strong> is a branch of artificial intelligence that focuses on machine reading comprehension. It enables AI agents to understand and interpret human language in a way that&#x27;s both meaningful and actionable.</p> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="key-components-of-nlu"></div><div class="heading-text"><div id="section-key-components-of-nlu" class="heading-anchor_backwardsCompatibility"></div>Key Components of NLU</div><a aria-label="Skip link to Key Components of NLU" class="heading-anchor-icon fa fa-anchor" href="#key-components-of-nlu"></a></h3> <ul> <li><strong>Intent Recognition</strong>: Determining the user&#x27;s goal or purpose.</li> <li><strong>Entity Extraction</strong>: Identifying and extracting specific pieces of information from the input (e.g., dates, names, locations).</li> <li><strong>Contextual Understanding</strong>: Interpreting the input based on the context of the conversation.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="intents-the-building-blocks-of-understanding"></div><div class="heading-text"><div id="section-intents-the-building-blocks-of-understanding" class="heading-anchor_backwardsCompatibility"></div>Intents: The Building Blocks of Understanding</div><a aria-label="Skip link to Intents: The Building Blocks of Understanding" class="heading-anchor-icon fa fa-anchor" href="#intents-the-building-blocks-of-understanding"></a></h3> <p><strong>Intents</strong> represent the actions or goals that users have when interacting with an AI agent. They are crucial for guiding the agent&#x27;s responses.</p> <ul> <li><strong>Example Intents</strong>: <ul> <li><strong>Greeting Intent</strong>: When a user says &quot;Hello&quot; or &quot;Hi.&quot;</li> <li><strong>Booking Intent</strong>: When a user wants to book a service or appointment.</li> <li><strong>Support Intent</strong>: When a user needs help with a product or service.</li> </ul> </li> </ul> <p>While this guide doesn&#x27;t delve deeply into intents, it&#x27;s important to recognize them as foundational elements in designing conversational agents.</p> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="enhancing-understanding-with-large-language-models-llms"></div><div class="heading-text"><div id="section-enhancing-understanding-with-large-language-models-ll-ms" class="heading-anchor_backwardsCompatibility"></div>Enhancing Understanding with Large Language Models (LLMs)</div><a aria-label="Skip link to Enhancing Understanding with Large Language Models (LLMs)" class="heading-anchor-icon fa fa-anchor" href="#enhancing-understanding-with-large-language-models-llms"></a></h2> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="what-are-llms"></div><div class="heading-text"><div id="section-what-are-ll-ms" class="heading-anchor_backwardsCompatibility"></div>What are LLMs?</div><a aria-label="Skip link to What are LLMs?" class="heading-anchor-icon fa fa-anchor" href="#what-are-llms"></a></h3> <p><strong>Large Language Models (LLMs)</strong> are advanced AI models trained on vast amounts of textual data. They excel at understanding and generating human-like text, making them powerful tools for interpreting user inputs.</p> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="llms-in-intent-classification"></div><div class="heading-text"><div id="section-ll-ms-in-intent-classification" class="heading-anchor_backwardsCompatibility"></div>LLMs in Intent Classification</div><a aria-label="Skip link to LLMs in Intent Classification" class="heading-anchor-icon fa fa-anchor" href="#llms-in-intent-classification"></a></h3> <p>By leveraging LLMs, AI agents can achieve higher accuracy in intent recognition. LLMs bring:</p> <ul> <li><strong>Contextual Awareness</strong>: Understanding nuances and context beyond keyword matching.</li> <li><strong>Flexibility</strong>: Interpreting a wide variety of phrasings and expressions.</li> <li><strong>Reduced Training Data Requirements</strong>: Performing effectively with fewer example inputs.</li> </ul> <p><span aria-label="" class="img lightbox closed" role="button" tabindex="0"><span class="lightbox-inner"><img alt="" loading="lazy" src="https://files.readme.io/570c079a030aaf268369a114bc80ef43a1f560116acc59a70e104e31cceaa730-image.png" align="" caption="" height="auto" title="" width="auto"/></span></span></p> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="hybrid-approach-combining-nlu-with-llms"></div><div class="heading-text"><div id="section-hybrid-approach-combining-nlu-with-ll-ms" class="heading-anchor_backwardsCompatibility"></div>Hybrid Approach: Combining NLU with LLMs</div><a aria-label="Skip link to Hybrid Approach: Combining NLU with LLMs" class="heading-anchor-icon fa fa-anchor" href="#hybrid-approach-combining-nlu-with-llms"></a></h3> <p>A hybrid methodology blends traditional NLU techniques with the capabilities of LLMs to create a more robust intent classification system. This approach offers:</p> <ul> <li><strong>Predictability</strong>: Reliability in recognizing known intents.</li> <li><strong>Adaptability</strong>: Ability to handle unexpected inputs gracefully.</li> <li><strong>Efficiency</strong>: Streamlined agent building with minimal training data.</li> </ul> <p>For a detailed exploration, refer to our guide on <a href="#" target="" title="">LLM Intent Classification</a>.</p> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="memory-and-context-in-conversations"></div><div class="heading-text"><div id="section-memory-and-context-in-conversations" class="heading-anchor_backwardsCompatibility"></div>Memory and Context in Conversations</div><a aria-label="Skip link to Memory and Context in Conversations" class="heading-anchor-icon fa fa-anchor" href="#memory-and-context-in-conversations"></a></h2> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="the-role-of-memory"></div><div class="heading-text"><div id="section-the-role-of-memory" class="heading-anchor_backwardsCompatibility"></div>The Role of Memory</div><a aria-label="Skip link to The Role of Memory" class="heading-anchor-icon fa fa-anchor" href="#the-role-of-memory"></a></h3> <p><strong>Memory</strong> in conversational AI refers to the agent&#x27;s ability to retain information from previous interactions within a conversation. This includes:</p> <ul> <li><strong>User&#x27;s Past Inputs</strong>: What the user has said earlier in the conversation.</li> <li><strong>Agent&#x27;s Previous Responses</strong>: The replies given by the agent.</li> <li><strong>Stored Variables</strong>: Specific data points captured for later use (e.g., user preferences, account details).</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="using-memory-to-enhance-understanding"></div><div class="heading-text"><div id="section-using-memory-to-enhance-understanding" class="heading-anchor_backwardsCompatibility"></div>Using Memory to Enhance Understanding</div><a aria-label="Skip link to Using Memory to Enhance Understanding" class="heading-anchor-icon fa fa-anchor" href="#using-memory-to-enhance-understanding"></a></h3> <p>By maintaining context, AI agents can:</p> <ul> <li><strong>Provide Relevant Responses</strong>: Tailor replies based on prior information.</li> <li><strong>Disambiguate Intent</strong>: Use previous inputs to clarify ambiguous queries.</li> <li><strong>Improve User Experience</strong>: Create a seamless and coherent conversation flow.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="implementing-memory-in-voiceflow"></div><div class="heading-text"><div id="section-implementing-memory-in-voiceflow" class="heading-anchor_backwardsCompatibility"></div>Implementing Memory in Voiceflow</div><a aria-label="Skip link to Implementing Memory in Voiceflow" class="heading-anchor-icon fa fa-anchor" href="#implementing-memory-in-voiceflow"></a></h3> <p>In Voiceflow, memory is managed through variables that store user inputs and other relevant data. These variables can be:</p> <ul> <li><strong>System Variables</strong>: Automatically maintained by the system (e.g., <button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang- theme-light" data-lang="" name="" tabindex="0">{user.last_input}</code>).</li> <li><strong>Custom Variables</strong>: Defined by the designer to capture specific information.</li> </ul> <p>These variables can be passed as context to the AI when generating responses, ensuring that the conversation remains coherent and personalized.</p> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="user-inputs-beyond-text"></div><div class="heading-text"><div id="section-user-inputs-beyond-text" class="heading-anchor_backwardsCompatibility"></div>User Inputs Beyond Text</div><a aria-label="Skip link to User Inputs Beyond Text" class="heading-anchor-icon fa fa-anchor" href="#user-inputs-beyond-text"></a></h2> <p>While natural language is a primary mode of interaction, users can also communicate with AI agents through other means:</p> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="buttons-and-quick-replies"></div><div class="heading-text"><div id="section-buttons-and-quick-replies" class="heading-anchor_backwardsCompatibility"></div>Buttons and Quick Replies</div><a aria-label="Skip link to Buttons and Quick Replies" class="heading-anchor-icon fa fa-anchor" href="#buttons-and-quick-replies"></a></h3> <ul> <li><strong>Purpose</strong>: Provide users with predefined options to select from.</li> <li><strong>Benefits</strong>: <ul> <li><strong>Simplify Choices</strong>: Reduce the cognitive load on users by presenting clear options.</li> <li><strong>Improve Accuracy</strong>: Minimize misunderstandings by limiting inputs to expected responses.</li> </ul> </li> <li><strong>Implementation</strong>: Incorporate buttons or quick reply options in the conversation design for actions like confirming choices, selecting from a list, or navigating menus.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="forms-and-structured-inputs"></div><div class="heading-text"><div id="section-forms-and-structured-inputs" class="heading-anchor_backwardsCompatibility"></div>Forms and Structured Inputs</div><a aria-label="Skip link to Forms and Structured Inputs" class="heading-anchor-icon fa fa-anchor" href="#forms-and-structured-inputs"></a></h3> <ul> <li><strong>Use Cases</strong>: Collecting specific information like contact details, feedback, or survey responses.</li> <li><strong>Advantages</strong>: <ul> <li><strong>Data Validation</strong>: Ensure inputs meet required formats (e.g., email addresses, phone numbers).</li> <li><strong>Efficiency</strong>: Streamline data collection in a user-friendly manner.</li> </ul> </li> </ul> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="handling-no-reply-and-no-match-globally"></div><div class="heading-text"><div id="section-handling-no-reply-and-no-match-globally" class="heading-anchor_backwardsCompatibility"></div>Handling No Reply and No Match Globally</div><a aria-label="Skip link to Handling No Reply and No Match Globally" class="heading-anchor-icon fa fa-anchor" href="#handling-no-reply-and-no-match-globally"></a></h2> <p>In conversational AI, it&#x27;s essential to manage situations where the user doesn&#x27;t respond or provides input that the agent doesn&#x27;t understand. Two key components for handling these scenarios are <strong>Global No Reply</strong> and <strong>Global No Match</strong>.</p> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="global-no-reply"></div><div class="heading-text"><div id="section-global-no-reply" class="heading-anchor_backwardsCompatibility"></div>Global No Reply</div><a aria-label="Skip link to Global No Reply" class="heading-anchor-icon fa fa-anchor" href="#global-no-reply"></a></h3> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="understanding-global-no-reply"></div><div class="heading-text"><div id="section-understanding-global-no-reply" class="heading-anchor_backwardsCompatibility"></div>Understanding Global No Reply</div><a aria-label="Skip link to Understanding Global No Reply" class="heading-anchor-icon fa fa-anchor" href="#understanding-global-no-reply"></a></h3> <p><strong>Global No Reply</strong> refers to how an AI agent handles situations where the user remains silent or inactive for a specified period. This could happen if the user steps away, gets distracted, or experiences technical issues.</p> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="role-in-the-understanding-engine"></div><div class="heading-text"><div id="section-role-in-the-understanding-engine" class="heading-anchor_backwardsCompatibility"></div>Role in the Understanding Engine</div><a aria-label="Skip link to Role in the Understanding Engine" class="heading-anchor-icon fa fa-anchor" href="#role-in-the-understanding-engine"></a></h3> <ul> <li><strong>Maintaining Engagement</strong>: By detecting user inactivity, the agent can decide whether to prompt the user again, wait longer, or end the conversation politely.</li> <li><strong>User Experience</strong>: A thoughtful response to no reply scenarios ensures users don&#x27;t feel ignored or confused about what to do next.</li> <li><strong>Context Awareness</strong>: The agent considers the conversation&#x27;s state to provide an appropriate reaction to silence, maintaining the conversation&#x27;s flow.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="global-no-match"></div><div class="heading-text"><div id="section-global-no-match" class="heading-anchor_backwardsCompatibility"></div>Global No Match</div><a aria-label="Skip link to Global No Match" class="heading-anchor-icon fa fa-anchor" href="#global-no-match"></a></h3> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="understanding-global-no-match"></div><div class="heading-text"><div id="section-understanding-global-no-match" class="heading-anchor_backwardsCompatibility"></div>Understanding Global No Match</div><a aria-label="Skip link to Understanding Global No Match" class="heading-anchor-icon fa fa-anchor" href="#understanding-global-no-match"></a></h3> <p><strong>Global No Match</strong> addresses situations where the user&#x27;s input doesn&#x27;t match any of the defined intents or expected responses. It acts as a safety net, ensuring that the agent can handle unexpected or unrecognized inputs gracefully.</p> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="role-in-the-understanding-engine-1"></div><div class="heading-text"><div id="section-role-in-the-understanding-engine" class="heading-anchor_backwardsCompatibility"></div>Role in the Understanding Engine</div><a aria-label="Skip link to Role in the Understanding Engine" class="heading-anchor-icon fa fa-anchor" href="#role-in-the-understanding-engine-1"></a></h3> <ul> <li><strong>Fallback Mechanism</strong>: Provides a default response or action when the agent cannot interpret the user&#x27;s input.</li> <li><strong>User Guidance</strong>: Helps redirect the user back to a known conversation path or offers assistance.</li> <li><strong>Error Handling</strong>: Prevents the conversation from stalling or ending abruptly due to misunderstandings.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="importance-in-agent-design"></div><div class="heading-text"><div id="section-importance-in-agent-design" class="heading-anchor_backwardsCompatibility"></div>Importance in Agent Design</div><a aria-label="Skip link to Importance in Agent Design" class="heading-anchor-icon fa fa-anchor" href="#importance-in-agent-design"></a></h3> <ul> <li><strong>Consistency</strong>: Global No Reply and Global No Match ensure that unhandled scenarios are managed uniformly across the entire conversation.</li> <li><strong>Resilience</strong>: They enhance the agent&#x27;s robustness, allowing it to handle a wide range of user behaviors and inputs.</li> <li><strong>Improved User Satisfaction</strong>: By thoughtfully managing these situations, users are less likely to feel frustrated or abandoned, leading to a better overall experience.</li> </ul> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="the-importance-of-a-good-understanding-model"></div><div class="heading-text"><div id="section-the-importance-of-a-good-understanding-model" class="heading-anchor_backwardsCompatibility"></div>The Importance of a Good Understanding Model</div><a aria-label="Skip link to The Importance of a Good Understanding Model" class="heading-anchor-icon fa fa-anchor" href="#the-importance-of-a-good-understanding-model"></a></h2> <p>A high-performing understanding model is critical for several reasons:</p> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="enhances-user-satisfaction"></div><div class="heading-text"><div id="section-enhances-user-satisfaction" class="heading-anchor_backwardsCompatibility"></div><strong>Enhances User Satisfaction</strong></div><a aria-label="Skip link to [object Object]" class="heading-anchor-icon fa fa-anchor" href="#enhances-user-satisfaction"></a></h3> <ul> <li><strong>Accuracy</strong>: Correctly interpreting user inputs leads to appropriate responses.</li> <li><strong>Efficiency</strong>: Reduces the need for repeated clarifications, saving time for the user.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="builds-trust"></div><div class="heading-text"><div id="section-builds-trust" class="heading-anchor_backwardsCompatibility"></div><strong>Builds Trust</strong></div><a aria-label="Skip link to [object Object]" class="heading-anchor-icon fa fa-anchor" href="#builds-trust"></a></h3> <ul> <li><strong>Reliability</strong>: Consistent understanding fosters user confidence in the AI agent.</li> <li><strong>Personalization</strong>: Remembering past interactions creates a more personalized experience.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="improves-business-outcomes"></div><div class="heading-text"><div id="section-improves-business-outcomes" class="heading-anchor_backwardsCompatibility"></div><strong>Improves Business Outcomes</strong></div><a aria-label="Skip link to [object Object]" class="heading-anchor-icon fa fa-anchor" href="#improves-business-outcomes"></a></h3> <ul> <li><strong>Goal Achievement</strong>: Efficiently guiding users to complete tasks (e.g., making a purchase, resolving an issue).</li> <li><strong>Data Collection</strong>: Accurately capturing information that can inform business decisions.</li> </ul> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="utilizing-tools-and-techniques-for-better-understanding"></div><div class="heading-text"><div id="section-utilizing-tools-and-techniques-for-better-understanding" class="heading-anchor_backwardsCompatibility"></div>Utilizing Tools and Techniques for Better Understanding</div><a aria-label="Skip link to Utilizing Tools and Techniques for Better Understanding" class="heading-anchor-icon fa fa-anchor" href="#utilizing-tools-and-techniques-for-better-understanding"></a></h2> <p>To develop a high-performing understanding component in your AI agent, consider leveraging the following tools and techniques:</p> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="1-intents-and-entities"></div><div class="heading-text"><div id="section-1-intents-and-entities" class="heading-anchor_backwardsCompatibility"></div>1. Intents and Entities</div><a aria-label="Skip link to 1. Intents and Entities" class="heading-anchor-icon fa fa-anchor" href="#1-intents-and-entities"></a></h3> <ul> <li><strong>Define Clear Intents</strong>: Map out the primary purposes users have when interacting with your agent.</li> <li><strong>Identify Entities</strong>: Determine the specific pieces of information needed to fulfill each intent.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="2-llm-based-intent-classification"></div><div class="heading-text"><div id="section-2-llm-based-intent-classification" class="heading-anchor_backwardsCompatibility"></div>2. LLM-Based Intent Classification</div><a aria-label="Skip link to 2. LLM-Based Intent Classification" class="heading-anchor-icon fa fa-anchor" href="#2-llm-based-intent-classification"></a></h3> <ul> <li><strong>Implement LLMs</strong>: Use large language models to enhance intent recognition accuracy.</li> <li><strong>Hybrid Models</strong>: Combine traditional NLU with LLMs for a balanced approach.</li> <li><strong>Customization</strong>: Tailor the LLM&#x27;s understanding through intent descriptions and example utterances.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="3-memory-management"></div><div class="heading-text"><div id="section-3-memory-management" class="heading-anchor_backwardsCompatibility"></div>3. Memory Management</div><a aria-label="Skip link to 3. Memory Management" class="heading-anchor-icon fa fa-anchor" href="#3-memory-management"></a></h3> <ul> <li><strong>Use Variables</strong>: Store important information for use throughout the conversation.</li> <li><strong>Contextual Responses</strong>: Reference past inputs to provide coherent and relevant replies.</li> <li><strong>Persistent Memory</strong>: Consider what information should persist beyond a single session for returning users.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="4-user-input-methods"></div><div class="heading-text"><div id="section-4-user-input-methods" class="heading-anchor_backwardsCompatibility"></div>4. User Input Methods</div><a aria-label="Skip link to 4. User Input Methods" class="heading-anchor-icon fa fa-anchor" href="#4-user-input-methods"></a></h3> <ul> <li><strong>Buttons</strong>: Simplify interactions and reduce errors.</li> <li><strong>Fallback Mechanisms</strong>: Prepare for unexpected inputs with default responses or clarifying questions. </li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="5-continuous-improvement"></div><div class="heading-text"><div id="section-5-continuous-improvement" class="heading-anchor_backwardsCompatibility"></div>5. Continuous Improvement</div><a aria-label="Skip link to 5. Continuous Improvement" class="heading-anchor-icon fa fa-anchor" href="#5-continuous-improvement"></a></h3> <ul> <li><strong>Monitor Performance</strong>: Use analytics to track how well your agent understands users.</li> <li><strong>Gather Feedback</strong>: Implement mechanisms for users to report misunderstandings.</li> <li><strong>Iterate Design</strong>: Regularly update intents, entities, and responses based on insights.</li> </ul> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="conclusion"></div><div class="heading-text"><div id="section-conclusion" class="heading-anchor_backwardsCompatibility"></div>Conclusion</div><a aria-label="Skip link to Conclusion" class="heading-anchor-icon fa fa-anchor" href="#conclusion"></a></h2> <p>Understanding user inputs is at the heart of effective conversational AI. By combining foundational elements like intents and entities with advanced technologies like LLMs and robust memory management, you can design AI agents that interact with users naturally and efficiently.</p> <p>Remember, the goal is to create an agent that doesn&#x27;t just respond but understands鈥攃apturing the nuances of human language and providing meaningful, context-aware interactions. By prioritizing the understanding component of your agent design, you set the stage for high-performing, user-centric conversations.</p> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="additional-resources"></div><div class="heading-text"><div id="section-additional-resources" class="heading-anchor_backwardsCompatibility"></div>Additional Resources</div><a aria-label="Skip link to Additional Resources" class="heading-anchor-icon fa fa-anchor" href="#additional-resources"></a></h2> <ul> <li><strong><a href="https://docs.voiceflow.com/docs/intents" target="" title="">Introduction to Intents</a></strong>: Dive deeper into defining and using intents effectively in your conversational designs.</li> <li><strong><a href="https://docs.voiceflow.com/docs/entities" target="" title="">Introduction to Entities</a></strong>: Dive deeper into defining and using intents effectively in your conversational designs.</li> <li><strong><a href="https://docs.voiceflow.com/docs/llm-intent-classification-method" target="" title="">LLM Intent Classification</a></strong>: Learn how to implement and customize LLMs for enhanced intent recognition.</li> <li><strong><a href="https://docs.voiceflow.com/docs/memory" target="" title="">Memory Management in Voiceflow</a></strong>: Explore how to use variables and context to maintain conversation flow.</li> </ul>" style="margin-left:31px" class="rm-Markdown markdown-body rm-Markdown markdown-body ng-non-bindable" data-testid="RDMD"><h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="introduction"></div><div class="heading-text"><div id="section-introduction" class="heading-anchor_backwardsCompatibility"></div>Introduction</div><a aria-label="Skip link to Introduction" class="heading-anchor-icon fa fa-anchor" href="#introduction"></a></h2> <p>In the rapidly evolving field of conversational AI, one of the most critical aspects is enabling AI agents to <strong>understand user inputs</strong> effectively. Whether it's a simple greeting, a complex question, or selecting a button in a user interface, understanding the user's intent is paramount for creating meaningful and efficient interactions.</p> <p>This guide provides a high-level overview of how AI agents interpret and process user inputs, especially natural language, to deliver appropriate responses. We'll explore the fundamental concepts that empower AI agents to comprehend user queries, including <strong>Intents</strong>, <strong>Natural Language Understanding (NLU)</strong>, <strong>Large Language Models (LLMs)</strong>, <strong>Memory and Context</strong>, and various input methods like button selections.</p> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="what-does-it-mean-for-an-ai-agent-to-understand-a-users-query"></div><div class="heading-text"><div id="section-what-does-it-mean-for-an-ai-agent-to-understand-a-users-query" class="heading-anchor_backwardsCompatibility"></div>What Does It Mean for an AI Agent to Understand a User's Query?</div><a aria-label="Skip link to What Does It Mean for an AI Agent to Understand a User's Query?" class="heading-anchor-icon fa fa-anchor" href="#what-does-it-mean-for-an-ai-agent-to-understand-a-users-query"></a></h2> <p>Understanding a user's query involves several layers of processing:</p> <ol> <li><strong>Capturing the Input</strong>: Receiving the user's message, which could be text, voice, or a selection.</li> <li><strong>Interpreting the Input</strong>: Analyzing the input to determine what the user wants or needs.</li> <li><strong>Determining the Intent</strong>: Identifying the purpose behind the user's message.</li> <li><strong>Extracting Relevant Information</strong>: Pulling out specific details or entities that are important for the response.</li> <li><strong>Generating an Appropriate Response</strong>: Crafting a reply that addresses the user's intent accurately and helpfully.</li> </ol> <p>An AI agent's ability to perform these steps effectively defines its <strong>understanding</strong> of the user. This process relies on various technologies and methodologies, which we'll explore in the following sections.</p> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="natural-language-understanding-nlu"></div><div class="heading-text"><div id="section-natural-language-understanding-nlu" class="heading-anchor_backwardsCompatibility"></div>Natural Language Understanding (NLU)</div><a aria-label="Skip link to Natural Language Understanding (NLU)" class="heading-anchor-icon fa fa-anchor" href="#natural-language-understanding-nlu"></a></h2> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="what-is-nlu"></div><div class="heading-text"><div id="section-what-is-nlu" class="heading-anchor_backwardsCompatibility"></div>What is NLU?</div><a aria-label="Skip link to What is NLU?" class="heading-anchor-icon fa fa-anchor" href="#what-is-nlu"></a></h3> <p><strong>Natural Language Understanding (NLU)</strong> is a branch of artificial intelligence that focuses on machine reading comprehension. It enables AI agents to understand and interpret human language in a way that's both meaningful and actionable.</p> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="key-components-of-nlu"></div><div class="heading-text"><div id="section-key-components-of-nlu" class="heading-anchor_backwardsCompatibility"></div>Key Components of NLU</div><a aria-label="Skip link to Key Components of NLU" class="heading-anchor-icon fa fa-anchor" href="#key-components-of-nlu"></a></h3> <ul> <li><strong>Intent Recognition</strong>: Determining the user's goal or purpose.</li> <li><strong>Entity Extraction</strong>: Identifying and extracting specific pieces of information from the input (e.g., dates, names, locations).</li> <li><strong>Contextual Understanding</strong>: Interpreting the input based on the context of the conversation.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="intents-the-building-blocks-of-understanding"></div><div class="heading-text"><div id="section-intents-the-building-blocks-of-understanding" class="heading-anchor_backwardsCompatibility"></div>Intents: The Building Blocks of Understanding</div><a aria-label="Skip link to Intents: The Building Blocks of Understanding" class="heading-anchor-icon fa fa-anchor" href="#intents-the-building-blocks-of-understanding"></a></h3> <p><strong>Intents</strong> represent the actions or goals that users have when interacting with an AI agent. They are crucial for guiding the agent's responses.</p> <ul> <li><strong>Example Intents</strong>: <ul> <li><strong>Greeting Intent</strong>: When a user says "Hello" or "Hi."</li> <li><strong>Booking Intent</strong>: When a user wants to book a service or appointment.</li> <li><strong>Support Intent</strong>: When a user needs help with a product or service.</li> </ul> </li> </ul> <p>While this guide doesn't delve deeply into intents, it's important to recognize them as foundational elements in designing conversational agents.</p> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="enhancing-understanding-with-large-language-models-llms"></div><div class="heading-text"><div id="section-enhancing-understanding-with-large-language-models-ll-ms" class="heading-anchor_backwardsCompatibility"></div>Enhancing Understanding with Large Language Models (LLMs)</div><a aria-label="Skip link to Enhancing Understanding with Large Language Models (LLMs)" class="heading-anchor-icon fa fa-anchor" href="#enhancing-understanding-with-large-language-models-llms"></a></h2> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="what-are-llms"></div><div class="heading-text"><div id="section-what-are-ll-ms" class="heading-anchor_backwardsCompatibility"></div>What are LLMs?</div><a aria-label="Skip link to What are LLMs?" class="heading-anchor-icon fa fa-anchor" href="#what-are-llms"></a></h3> <p><strong>Large Language Models (LLMs)</strong> are advanced AI models trained on vast amounts of textual data. They excel at understanding and generating human-like text, making them powerful tools for interpreting user inputs.</p> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="llms-in-intent-classification"></div><div class="heading-text"><div id="section-ll-ms-in-intent-classification" class="heading-anchor_backwardsCompatibility"></div>LLMs in Intent Classification</div><a aria-label="Skip link to LLMs in Intent Classification" class="heading-anchor-icon fa fa-anchor" href="#llms-in-intent-classification"></a></h3> <p>By leveraging LLMs, AI agents can achieve higher accuracy in intent recognition. LLMs bring:</p> <ul> <li><strong>Contextual Awareness</strong>: Understanding nuances and context beyond keyword matching.</li> <li><strong>Flexibility</strong>: Interpreting a wide variety of phrasings and expressions.</li> <li><strong>Reduced Training Data Requirements</strong>: Performing effectively with fewer example inputs.</li> </ul> <p><span aria-label="" class="img lightbox closed" role="button" tabindex="0"><span class="lightbox-inner"><img alt="" loading="lazy" src="https://files.readme.io/570c079a030aaf268369a114bc80ef43a1f560116acc59a70e104e31cceaa730-image.png" align="" caption="" height="auto" title="" width="auto"/></span></span></p> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="hybrid-approach-combining-nlu-with-llms"></div><div class="heading-text"><div id="section-hybrid-approach-combining-nlu-with-ll-ms" class="heading-anchor_backwardsCompatibility"></div>Hybrid Approach: Combining NLU with LLMs</div><a aria-label="Skip link to Hybrid Approach: Combining NLU with LLMs" class="heading-anchor-icon fa fa-anchor" href="#hybrid-approach-combining-nlu-with-llms"></a></h3> <p>A hybrid methodology blends traditional NLU techniques with the capabilities of LLMs to create a more robust intent classification system. This approach offers:</p> <ul> <li><strong>Predictability</strong>: Reliability in recognizing known intents.</li> <li><strong>Adaptability</strong>: Ability to handle unexpected inputs gracefully.</li> <li><strong>Efficiency</strong>: Streamlined agent building with minimal training data.</li> </ul> <p>For a detailed exploration, refer to our guide on <a target="_self" href="#">LLM Intent Classification</a>.</p> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="memory-and-context-in-conversations"></div><div class="heading-text"><div id="section-memory-and-context-in-conversations" class="heading-anchor_backwardsCompatibility"></div>Memory and Context in Conversations</div><a aria-label="Skip link to Memory and Context in Conversations" class="heading-anchor-icon fa fa-anchor" href="#memory-and-context-in-conversations"></a></h2> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="the-role-of-memory"></div><div class="heading-text"><div id="section-the-role-of-memory" class="heading-anchor_backwardsCompatibility"></div>The Role of Memory</div><a aria-label="Skip link to The Role of Memory" class="heading-anchor-icon fa fa-anchor" href="#the-role-of-memory"></a></h3> <p><strong>Memory</strong> in conversational AI refers to the agent's ability to retain information from previous interactions within a conversation. This includes:</p> <ul> <li><strong>User's Past Inputs</strong>: What the user has said earlier in the conversation.</li> <li><strong>Agent's Previous Responses</strong>: The replies given by the agent.</li> <li><strong>Stored Variables</strong>: Specific data points captured for later use (e.g., user preferences, account details).</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="using-memory-to-enhance-understanding"></div><div class="heading-text"><div id="section-using-memory-to-enhance-understanding" class="heading-anchor_backwardsCompatibility"></div>Using Memory to Enhance Understanding</div><a aria-label="Skip link to Using Memory to Enhance Understanding" class="heading-anchor-icon fa fa-anchor" href="#using-memory-to-enhance-understanding"></a></h3> <p>By maintaining context, AI agents can:</p> <ul> <li><strong>Provide Relevant Responses</strong>: Tailor replies based on prior information.</li> <li><strong>Disambiguate Intent</strong>: Use previous inputs to clarify ambiguous queries.</li> <li><strong>Improve User Experience</strong>: Create a seamless and coherent conversation flow.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="implementing-memory-in-voiceflow"></div><div class="heading-text"><div id="section-implementing-memory-in-voiceflow" class="heading-anchor_backwardsCompatibility"></div>Implementing Memory in Voiceflow</div><a aria-label="Skip link to Implementing Memory in Voiceflow" class="heading-anchor-icon fa fa-anchor" href="#implementing-memory-in-voiceflow"></a></h3> <p>In Voiceflow, memory is managed through variables that store user inputs and other relevant data. These variables can be:</p> <ul> <li><strong>System Variables</strong>: Automatically maintained by the system (e.g., <button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang- theme-light" data-lang="" name="" tabindex="0">{user.last_input}</code>).</li> <li><strong>Custom Variables</strong>: Defined by the designer to capture specific information.</li> </ul> <p>These variables can be passed as context to the AI when generating responses, ensuring that the conversation remains coherent and personalized.</p> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="user-inputs-beyond-text"></div><div class="heading-text"><div id="section-user-inputs-beyond-text" class="heading-anchor_backwardsCompatibility"></div>User Inputs Beyond Text</div><a aria-label="Skip link to User Inputs Beyond Text" class="heading-anchor-icon fa fa-anchor" href="#user-inputs-beyond-text"></a></h2> <p>While natural language is a primary mode of interaction, users can also communicate with AI agents through other means:</p> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="buttons-and-quick-replies"></div><div class="heading-text"><div id="section-buttons-and-quick-replies" class="heading-anchor_backwardsCompatibility"></div>Buttons and Quick Replies</div><a aria-label="Skip link to Buttons and Quick Replies" class="heading-anchor-icon fa fa-anchor" href="#buttons-and-quick-replies"></a></h3> <ul> <li><strong>Purpose</strong>: Provide users with predefined options to select from.</li> <li><strong>Benefits</strong>: <ul> <li><strong>Simplify Choices</strong>: Reduce the cognitive load on users by presenting clear options.</li> <li><strong>Improve Accuracy</strong>: Minimize misunderstandings by limiting inputs to expected responses.</li> </ul> </li> <li><strong>Implementation</strong>: Incorporate buttons or quick reply options in the conversation design for actions like confirming choices, selecting from a list, or navigating menus.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="forms-and-structured-inputs"></div><div class="heading-text"><div id="section-forms-and-structured-inputs" class="heading-anchor_backwardsCompatibility"></div>Forms and Structured Inputs</div><a aria-label="Skip link to Forms and Structured Inputs" class="heading-anchor-icon fa fa-anchor" href="#forms-and-structured-inputs"></a></h3> <ul> <li><strong>Use Cases</strong>: Collecting specific information like contact details, feedback, or survey responses.</li> <li><strong>Advantages</strong>: <ul> <li><strong>Data Validation</strong>: Ensure inputs meet required formats (e.g., email addresses, phone numbers).</li> <li><strong>Efficiency</strong>: Streamline data collection in a user-friendly manner.</li> </ul> </li> </ul> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="handling-no-reply-and-no-match-globally"></div><div class="heading-text"><div id="section-handling-no-reply-and-no-match-globally" class="heading-anchor_backwardsCompatibility"></div>Handling No Reply and No Match Globally</div><a aria-label="Skip link to Handling No Reply and No Match Globally" class="heading-anchor-icon fa fa-anchor" href="#handling-no-reply-and-no-match-globally"></a></h2> <p>In conversational AI, it's essential to manage situations where the user doesn't respond or provides input that the agent doesn't understand. Two key components for handling these scenarios are <strong>Global No Reply</strong> and <strong>Global No Match</strong>.</p> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="global-no-reply"></div><div class="heading-text"><div id="section-global-no-reply" class="heading-anchor_backwardsCompatibility"></div>Global No Reply</div><a aria-label="Skip link to Global No Reply" class="heading-anchor-icon fa fa-anchor" href="#global-no-reply"></a></h3> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="understanding-global-no-reply"></div><div class="heading-text"><div id="section-understanding-global-no-reply" class="heading-anchor_backwardsCompatibility"></div>Understanding Global No Reply</div><a aria-label="Skip link to Understanding Global No Reply" class="heading-anchor-icon fa fa-anchor" href="#understanding-global-no-reply"></a></h3> <p><strong>Global No Reply</strong> refers to how an AI agent handles situations where the user remains silent or inactive for a specified period. This could happen if the user steps away, gets distracted, or experiences technical issues.</p> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="role-in-the-understanding-engine"></div><div class="heading-text"><div id="section-role-in-the-understanding-engine" class="heading-anchor_backwardsCompatibility"></div>Role in the Understanding Engine</div><a aria-label="Skip link to Role in the Understanding Engine" class="heading-anchor-icon fa fa-anchor" href="#role-in-the-understanding-engine"></a></h3> <ul> <li><strong>Maintaining Engagement</strong>: By detecting user inactivity, the agent can decide whether to prompt the user again, wait longer, or end the conversation politely.</li> <li><strong>User Experience</strong>: A thoughtful response to no reply scenarios ensures users don't feel ignored or confused about what to do next.</li> <li><strong>Context Awareness</strong>: The agent considers the conversation's state to provide an appropriate reaction to silence, maintaining the conversation's flow.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="global-no-match"></div><div class="heading-text"><div id="section-global-no-match" class="heading-anchor_backwardsCompatibility"></div>Global No Match</div><a aria-label="Skip link to Global No Match" class="heading-anchor-icon fa fa-anchor" href="#global-no-match"></a></h3> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="understanding-global-no-match"></div><div class="heading-text"><div id="section-understanding-global-no-match" class="heading-anchor_backwardsCompatibility"></div>Understanding Global No Match</div><a aria-label="Skip link to Understanding Global No Match" class="heading-anchor-icon fa fa-anchor" href="#understanding-global-no-match"></a></h3> <p><strong>Global No Match</strong> addresses situations where the user's input doesn't match any of the defined intents or expected responses. It acts as a safety net, ensuring that the agent can handle unexpected or unrecognized inputs gracefully.</p> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="role-in-the-understanding-engine-1"></div><div class="heading-text"><div id="section-role-in-the-understanding-engine" class="heading-anchor_backwardsCompatibility"></div>Role in the Understanding Engine</div><a aria-label="Skip link to Role in the Understanding Engine" class="heading-anchor-icon fa fa-anchor" href="#role-in-the-understanding-engine-1"></a></h3> <ul> <li><strong>Fallback Mechanism</strong>: Provides a default response or action when the agent cannot interpret the user's input.</li> <li><strong>User Guidance</strong>: Helps redirect the user back to a known conversation path or offers assistance.</li> <li><strong>Error Handling</strong>: Prevents the conversation from stalling or ending abruptly due to misunderstandings.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="importance-in-agent-design"></div><div class="heading-text"><div id="section-importance-in-agent-design" class="heading-anchor_backwardsCompatibility"></div>Importance in Agent Design</div><a aria-label="Skip link to Importance in Agent Design" class="heading-anchor-icon fa fa-anchor" href="#importance-in-agent-design"></a></h3> <ul> <li><strong>Consistency</strong>: Global No Reply and Global No Match ensure that unhandled scenarios are managed uniformly across the entire conversation.</li> <li><strong>Resilience</strong>: They enhance the agent's robustness, allowing it to handle a wide range of user behaviors and inputs.</li> <li><strong>Improved User Satisfaction</strong>: By thoughtfully managing these situations, users are less likely to feel frustrated or abandoned, leading to a better overall experience.</li> </ul> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="the-importance-of-a-good-understanding-model"></div><div class="heading-text"><div id="section-the-importance-of-a-good-understanding-model" class="heading-anchor_backwardsCompatibility"></div>The Importance of a Good Understanding Model</div><a aria-label="Skip link to The Importance of a Good Understanding Model" class="heading-anchor-icon fa fa-anchor" href="#the-importance-of-a-good-understanding-model"></a></h2> <p>A high-performing understanding model is critical for several reasons:</p> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="enhances-user-satisfaction"></div><div class="heading-text"><div id="section-enhances-user-satisfaction" class="heading-anchor_backwardsCompatibility"></div><strong>Enhances User Satisfaction</strong></div><a aria-label="Skip link to [object Object]" class="heading-anchor-icon fa fa-anchor" href="#enhances-user-satisfaction"></a></h3> <ul> <li><strong>Accuracy</strong>: Correctly interpreting user inputs leads to appropriate responses.</li> <li><strong>Efficiency</strong>: Reduces the need for repeated clarifications, saving time for the user.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="builds-trust"></div><div class="heading-text"><div id="section-builds-trust" class="heading-anchor_backwardsCompatibility"></div><strong>Builds Trust</strong></div><a aria-label="Skip link to [object Object]" class="heading-anchor-icon fa fa-anchor" href="#builds-trust"></a></h3> <ul> <li><strong>Reliability</strong>: Consistent understanding fosters user confidence in the AI agent.</li> <li><strong>Personalization</strong>: Remembering past interactions creates a more personalized experience.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="improves-business-outcomes"></div><div class="heading-text"><div id="section-improves-business-outcomes" class="heading-anchor_backwardsCompatibility"></div><strong>Improves Business Outcomes</strong></div><a aria-label="Skip link to [object Object]" class="heading-anchor-icon fa fa-anchor" href="#improves-business-outcomes"></a></h3> <ul> <li><strong>Goal Achievement</strong>: Efficiently guiding users to complete tasks (e.g., making a purchase, resolving an issue).</li> <li><strong>Data Collection</strong>: Accurately capturing information that can inform business decisions.</li> </ul> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="utilizing-tools-and-techniques-for-better-understanding"></div><div class="heading-text"><div id="section-utilizing-tools-and-techniques-for-better-understanding" class="heading-anchor_backwardsCompatibility"></div>Utilizing Tools and Techniques for Better Understanding</div><a aria-label="Skip link to Utilizing Tools and Techniques for Better Understanding" class="heading-anchor-icon fa fa-anchor" href="#utilizing-tools-and-techniques-for-better-understanding"></a></h2> <p>To develop a high-performing understanding component in your AI agent, consider leveraging the following tools and techniques:</p> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="1-intents-and-entities"></div><div class="heading-text"><div id="section-1-intents-and-entities" class="heading-anchor_backwardsCompatibility"></div>1. Intents and Entities</div><a aria-label="Skip link to 1. Intents and Entities" class="heading-anchor-icon fa fa-anchor" href="#1-intents-and-entities"></a></h3> <ul> <li><strong>Define Clear Intents</strong>: Map out the primary purposes users have when interacting with your agent.</li> <li><strong>Identify Entities</strong>: Determine the specific pieces of information needed to fulfill each intent.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="2-llm-based-intent-classification"></div><div class="heading-text"><div id="section-2-llm-based-intent-classification" class="heading-anchor_backwardsCompatibility"></div>2. LLM-Based Intent Classification</div><a aria-label="Skip link to 2. LLM-Based Intent Classification" class="heading-anchor-icon fa fa-anchor" href="#2-llm-based-intent-classification"></a></h3> <ul> <li><strong>Implement LLMs</strong>: Use large language models to enhance intent recognition accuracy.</li> <li><strong>Hybrid Models</strong>: Combine traditional NLU with LLMs for a balanced approach.</li> <li><strong>Customization</strong>: Tailor the LLM's understanding through intent descriptions and example utterances.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="3-memory-management"></div><div class="heading-text"><div id="section-3-memory-management" class="heading-anchor_backwardsCompatibility"></div>3. Memory Management</div><a aria-label="Skip link to 3. Memory Management" class="heading-anchor-icon fa fa-anchor" href="#3-memory-management"></a></h3> <ul> <li><strong>Use Variables</strong>: Store important information for use throughout the conversation.</li> <li><strong>Contextual Responses</strong>: Reference past inputs to provide coherent and relevant replies.</li> <li><strong>Persistent Memory</strong>: Consider what information should persist beyond a single session for returning users.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="4-user-input-methods"></div><div class="heading-text"><div id="section-4-user-input-methods" class="heading-anchor_backwardsCompatibility"></div>4. User Input Methods</div><a aria-label="Skip link to 4. User Input Methods" class="heading-anchor-icon fa fa-anchor" href="#4-user-input-methods"></a></h3> <ul> <li><strong>Buttons</strong>: Simplify interactions and reduce errors.</li> <li><strong>Fallback Mechanisms</strong>: Prepare for unexpected inputs with default responses or clarifying questions. </li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="5-continuous-improvement"></div><div class="heading-text"><div id="section-5-continuous-improvement" class="heading-anchor_backwardsCompatibility"></div>5. Continuous Improvement</div><a aria-label="Skip link to 5. Continuous Improvement" class="heading-anchor-icon fa fa-anchor" href="#5-continuous-improvement"></a></h3> <ul> <li><strong>Monitor Performance</strong>: Use analytics to track how well your agent understands users.</li> <li><strong>Gather Feedback</strong>: Implement mechanisms for users to report misunderstandings.</li> <li><strong>Iterate Design</strong>: Regularly update intents, entities, and responses based on insights.</li> </ul> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="conclusion"></div><div class="heading-text"><div id="section-conclusion" class="heading-anchor_backwardsCompatibility"></div>Conclusion</div><a aria-label="Skip link to Conclusion" class="heading-anchor-icon fa fa-anchor" href="#conclusion"></a></h2> <p>Understanding user inputs is at the heart of effective conversational AI. By combining foundational elements like intents and entities with advanced technologies like LLMs and robust memory management, you can design AI agents that interact with users naturally and efficiently.</p> <p>Remember, the goal is to create an agent that doesn't just respond but understands鈥攃apturing the nuances of human language and providing meaningful, context-aware interactions. By prioritizing the understanding component of your agent design, you set the stage for high-performing, user-centric conversations.</p> <hr/> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="additional-resources"></div><div class="heading-text"><div id="section-additional-resources" class="heading-anchor_backwardsCompatibility"></div>Additional Resources</div><a aria-label="Skip link to Additional Resources" class="heading-anchor-icon fa fa-anchor" href="#additional-resources"></a></h2> <ul> <li><strong><a target="_self" href="/docs/intents">Introduction to Intents</a></strong>: Dive deeper into defining and using intents effectively in your conversational designs.</li> <li><strong><a target="_self" href="/docs/entities">Introduction to Entities</a></strong>: Dive deeper into defining and using intents effectively in your conversational designs.</li> <li><strong><a target="_self" href="/docs/llm-intent-classification-method">LLM Intent Classification</a></strong>: Learn how to implement and customize LLMs for enhanced intent recognition.</li> <li><strong><a target="_self" href="/docs/memory">Memory Management in Voiceflow</a></strong>: Explore how to use variables and context to maintain conversation flow.</li> </ul></div><div class="UpdatedAt"><p class="DateLine "><i class="icon icon-watch"></i>Updated 2 months ago </p></div><hr class="NextStepsDivider"/><nav aria-label="Pagination Controls" class="PaginationControlsjDYuqu8pBMUy rm-Pagination"></nav></section><section class="content-toc grid-25"><nav><ul class="toc-list"><li><a class="tocHeader" href="#"><i class="icon icon-text-align-left"></i>Table of Contents</a></li><li class="toc-children"><ul> <li> <a href="#introduction">Introduction</a> </li> <li> <a href="#what-does-it-mean-for-an-ai-agent-to-understand-a-users-query">What Does It Mean for an AI Agent to Understand a User's Query?</a> </li> <li> <a href="#natural-language-understanding-nlu">Natural Language Understanding (NLU)</a> <ul> <li><a href="#what-is-nlu">What is NLU?</a></li> <li><a href="#key-components-of-nlu">Key Components of NLU</a></li> <li><a href="#intents-the-building-blocks-of-understanding">Intents: The Building Blocks of Understanding</a></li> </ul> </li> <li> <a href="#enhancing-understanding-with-large-language-models-llms">Enhancing Understanding with Large Language Models (LLMs)</a> <ul> <li><a href="#what-are-llms">What are LLMs?</a></li> <li><a href="#llms-in-intent-classification">LLMs in Intent Classification</a></li> <li><a href="#hybrid-approach-combining-nlu-with-llms">Hybrid Approach: Combining NLU with LLMs</a></li> </ul> </li> <li> <a href="#memory-and-context-in-conversations">Memory and Context in Conversations</a> <ul> <li><a href="#the-role-of-memory">The Role of Memory</a></li> <li><a href="#using-memory-to-enhance-understanding">Using Memory to Enhance Understanding</a></li> <li><a href="#implementing-memory-in-voiceflow">Implementing Memory in Voiceflow</a></li> </ul> </li> <li> <a href="#user-inputs-beyond-text">User Inputs Beyond Text</a> <ul> <li><a href="#buttons-and-quick-replies">Buttons and Quick Replies</a></li> <li><a href="#forms-and-structured-inputs">Forms and Structured Inputs</a></li> </ul> </li> <li> <a href="#handling-no-reply-and-no-match-globally">Handling No Reply and No Match Globally</a> <ul> <li><a href="#global-no-reply">Global No Reply</a></li> <li><a href="#understanding-global-no-reply">Understanding Global No Reply</a></li> <li><a href="#role-in-the-understanding-engine">Role in the Understanding Engine</a></li> <li><a href="#global-no-match">Global No Match</a></li> <li><a href="#understanding-global-no-match">Understanding Global No Match</a></li> <li><a href="#role-in-the-understanding-engine-1">Role in the Understanding Engine</a></li> <li><a href="#importance-in-agent-design">Importance in Agent Design</a></li> </ul> </li> <li> <a href="#the-importance-of-a-good-understanding-model">The Importance of a Good Understanding Model</a> <ul> <li><a href="#enhances-user-satisfaction"><strong>Enhances User Satisfaction</strong></a></li> <li><a href="#builds-trust"><strong>Builds Trust</strong></a></li> <li><a href="#improves-business-outcomes"><strong>Improves Business Outcomes</strong></a></li> </ul> </li> <li> <a href="#utilizing-tools-and-techniques-for-better-understanding">Utilizing Tools and Techniques for Better Understanding</a> <ul> <li><a href="#1-intents-and-entities">1. Intents and Entities</a></li> <li><a href="#2-llm-based-intent-classification">2. LLM-Based Intent Classification</a></li> <li><a href="#3-memory-management">3. Memory Management</a></li> <li><a href="#4-user-input-methods">4. User Input Methods</a></li> <li><a href="#5-continuous-improvement">5. Continuous Improvement</a></li> </ul> </li> <li> <a href="#conclusion">Conclusion</a> </li> <li> <a href="#additional-resources">Additional Resources</a> </li> </ul></li></ul></nav></section></div><div class="ModalWrapper" id="tutorialmodal-root"></div></article></div></main><footer aria-label="Status banner" class="AppFooter rm-Banners"></footer><div class="ModalWrapper" id="ChatGPT-modal"></div></div></div><div class="ng-non-bindable"><script id="ssr-props" data-initial-props="{"sidebars":{"docs":[{"_id":"6659cc0ab80ec2004f1aa17c","title":"Getting Started","slug":"send-first-message","order":0,"reference":false,"isAPI":false,"project":"61f2f6237da66107c6124fbe","version":"6659cc0ab80ec2004f1aa284","createdAt":"2022-01-27T21:49:22.527Z","__v":0,"pages":[{"_id":"665e1e7f5425ce005157cb6d","api":{"method":"get","url":"","auth":"required","params":[]},"title":"Welcome to the 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Reference","changelog":"Changelog","discuss":"","tutorials":"","recipes":""}}],"UrlManager":{"options":{"child":false,"parent":null,"hasOneChild":false,"project":null,"version":null,"lang":"en"},"defaults":{"lang":"en"}},"urlManagerOpts":{"lang":"en","parent":{"childrenProjects":[]},"project":{"subdomain":"voiceflow-developer"},"version":"2.0"}},"activeDoc":"listen-fundamentals","rdmd":{"baseUrl":"/","body":"## Introduction\n\nIn the rapidly evolving field of conversational AI, one of the most critical aspects is enabling AI agents to **understand user inputs** effectively. Whether it's a simple greeting, a complex question, or selecting a button in a user interface, understanding the user's intent is paramount for creating meaningful and efficient interactions.\n\nThis guide provides a high-level overview of how AI agents interpret and process user inputs, especially natural language, to deliver appropriate responses. We'll explore the fundamental concepts that empower AI agents to comprehend user queries, including **Intents**, **Natural Language Understanding (NLU)**, **Large Language Models (LLMs)**, **Memory and Context**, and various input methods like button selections.\n\n***\n\n## What Does It Mean for an AI Agent to Understand a User's Query?\n\nUnderstanding a user's query involves several layers of processing:\n\n1. **Capturing the Input**: Receiving the user's message, which could be text, voice, or a selection.\n2. **Interpreting the Input**: Analyzing the input to determine what the user wants or needs.\n3. **Determining the Intent**: Identifying the purpose behind the user's message.\n4. **Extracting Relevant Information**: Pulling out specific details or entities that are important for the response.\n5. **Generating an Appropriate Response**: Crafting a reply that addresses the user's intent accurately and helpfully.\n\nAn AI agent's ability to perform these steps effectively defines its **understanding** of the user. This process relies on various technologies and methodologies, which we'll explore in the following sections.\n\n***\n\n## Natural Language Understanding (NLU)\n\n### What is NLU?\n\n**Natural Language Understanding (NLU)** is a branch of artificial intelligence that focuses on machine reading comprehension. It enables AI agents to understand and interpret human language in a way that's both meaningful and actionable.\n\n### Key Components of NLU\n\n- **Intent Recognition**: Determining the user's goal or purpose.\n- **Entity Extraction**: Identifying and extracting specific pieces of information from the input (e.g., dates, names, locations).\n- **Contextual Understanding**: Interpreting the input based on the context of the conversation.\n\n### Intents: The Building Blocks of Understanding\n\n**Intents** represent the actions or goals that users have when interacting with an AI agent. They are crucial for guiding the agent's responses.\n\n- **Example Intents**:\n - **Greeting Intent**: When a user says \"Hello\" or \"Hi.\"\n - **Booking Intent**: When a user wants to book a service or appointment.\n - **Support Intent**: When a user needs help with a product or service.\n\nWhile this guide doesn't delve deeply into intents, it's important to recognize them as foundational elements in designing conversational agents.\n\n***\n\n## Enhancing Understanding with Large Language Models (LLMs)\n\n### What are LLMs?\n\n**Large Language Models (LLMs)** are advanced AI models trained on vast amounts of textual data. They excel at understanding and generating human-like text, making them powerful tools for interpreting user inputs.\n\n### LLMs in Intent Classification\n\nBy leveraging LLMs, AI agents can achieve higher accuracy in intent recognition. LLMs bring:\n\n- **Contextual Awareness**: Understanding nuances and context beyond keyword matching.\n- **Flexibility**: Interpreting a wide variety of phrasings and expressions.\n- **Reduced Training Data Requirements**: Performing effectively with fewer example inputs.\n\n![](https://files.readme.io/570c079a030aaf268369a114bc80ef43a1f560116acc59a70e104e31cceaa730-image.png)\n\n### Hybrid Approach: Combining NLU with LLMs\n\nA hybrid methodology blends traditional NLU techniques with the capabilities of LLMs to create a more robust intent classification system. This approach offers:\n\n- **Predictability**: Reliability in recognizing known intents.\n- **Adaptability**: Ability to handle unexpected inputs gracefully.\n- **Efficiency**: Streamlined agent building with minimal training data.\n\nFor a detailed exploration, refer to our guide on [LLM Intent Classification](#).\n\n***\n\n## Memory and Context in Conversations\n\n### The Role of Memory\n\n**Memory** in conversational AI refers to the agent's ability to retain information from previous interactions within a conversation. This includes:\n\n- **User's Past Inputs**: What the user has said earlier in the conversation.\n- **Agent's Previous Responses**: The replies given by the agent.\n- **Stored Variables**: Specific data points captured for later use (e.g., user preferences, account details).\n\n### Using Memory to Enhance Understanding\n\nBy maintaining context, AI agents can:\n\n- **Provide Relevant Responses**: Tailor replies based on prior information.\n- **Disambiguate Intent**: Use previous inputs to clarify ambiguous queries.\n- **Improve User Experience**: Create a seamless and coherent conversation flow.\n\n### Implementing Memory in Voiceflow\n\nIn Voiceflow, memory is managed through variables that store user inputs and other relevant data. These variables can be:\n\n- **System Variables**: Automatically maintained by the system (e.g., `{user.last_input}`).\n- **Custom Variables**: Defined by the designer to capture specific information.\n\nThese variables can be passed as context to the AI when generating responses, ensuring that the conversation remains coherent and personalized.\n\n***\n\n## User Inputs Beyond Text\n\nWhile natural language is a primary mode of interaction, users can also communicate with AI agents through other means:\n\n### Buttons and Quick Replies\n\n- **Purpose**: Provide users with predefined options to select from.\n- **Benefits**:\n - **Simplify Choices**: Reduce the cognitive load on users by presenting clear options.\n - **Improve Accuracy**: Minimize misunderstandings by limiting inputs to expected responses.\n- **Implementation**: Incorporate buttons or quick reply options in the conversation design for actions like confirming choices, selecting from a list, or navigating menus.\n\n### Forms and Structured Inputs\n\n- **Use Cases**: Collecting specific information like contact details, feedback, or survey responses.\n- **Advantages**:\n - **Data Validation**: Ensure inputs meet required formats (e.g., email addresses, phone numbers).\n - **Efficiency**: Streamline data collection in a user-friendly manner.\n\n***\n\n## Handling No Reply and No Match Globally\n\nIn conversational AI, it's essential to manage situations where the user doesn't respond or provides input that the agent doesn't understand. Two key components for handling these scenarios are **Global No Reply** and **Global No Match**.\n\n### Global No Reply\n\n### Understanding Global No Reply\n\n**Global No Reply** refers to how an AI agent handles situations where the user remains silent or inactive for a specified period. This could happen if the user steps away, gets distracted, or experiences technical issues.\n\n### Role in the Understanding Engine\n\n- **Maintaining Engagement**: By detecting user inactivity, the agent can decide whether to prompt the user again, wait longer, or end the conversation politely.\n- **User Experience**: A thoughtful response to no reply scenarios ensures users don't feel ignored or confused about what to do next.\n- **Context Awareness**: The agent considers the conversation's state to provide an appropriate reaction to silence, maintaining the conversation's flow.\n\n### Global No Match\n\n### Understanding Global No Match\n\n**Global No Match** addresses situations where the user's input doesn't match any of the defined intents or expected responses. It acts as a safety net, ensuring that the agent can handle unexpected or unrecognized inputs gracefully.\n\n### Role in the Understanding Engine\n\n- **Fallback Mechanism**: Provides a default response or action when the agent cannot interpret the user's input.\n- **User Guidance**: Helps redirect the user back to a known conversation path or offers assistance.\n- **Error Handling**: Prevents the conversation from stalling or ending abruptly due to misunderstandings.\n\n### Importance in Agent Design\n\n- **Consistency**: Global No Reply and Global No Match ensure that unhandled scenarios are managed uniformly across the entire conversation.\n- **Resilience**: They enhance the agent's robustness, allowing it to handle a wide range of user behaviors and inputs.\n- **Improved User Satisfaction**: By thoughtfully managing these situations, users are less likely to feel frustrated or abandoned, leading to a better overall experience.\n\n***\n\n## The Importance of a Good Understanding Model\n\nA high-performing understanding model is critical for several reasons:\n\n### **Enhances User Satisfaction**\n\n- **Accuracy**: Correctly interpreting user inputs leads to appropriate responses.\n- **Efficiency**: Reduces the need for repeated clarifications, saving time for the user.\n\n### **Builds Trust**\n\n- **Reliability**: Consistent understanding fosters user confidence in the AI agent.\n- **Personalization**: Remembering past interactions creates a more personalized experience.\n\n### **Improves Business Outcomes**\n\n- **Goal Achievement**: Efficiently guiding users to complete tasks (e.g., making a purchase, resolving an issue).\n- **Data Collection**: Accurately capturing information that can inform business decisions.\n\n***\n\n## Utilizing Tools and Techniques for Better Understanding\n\nTo develop a high-performing understanding component in your AI agent, consider leveraging the following tools and techniques:\n\n### 1. Intents and Entities\n\n- **Define Clear Intents**: Map out the primary purposes users have when interacting with your agent.\n- **Identify Entities**: Determine the specific pieces of information needed to fulfill each intent.\n\n### 2. LLM-Based Intent Classification\n\n- **Implement LLMs**: Use large language models to enhance intent recognition accuracy.\n- **Hybrid Models**: Combine traditional NLU with LLMs for a balanced approach.\n- **Customization**: Tailor the LLM's understanding through intent descriptions and example utterances.\n\n### 3. Memory Management\n\n- **Use Variables**: Store important information for use throughout the conversation.\n- **Contextual Responses**: Reference past inputs to provide coherent and relevant replies.\n- **Persistent Memory**: Consider what information should persist beyond a single session for returning users.\n\n### 4. User Input Methods\n\n- **Buttons**: Simplify interactions and reduce errors.\n- **Fallback Mechanisms**: Prepare for unexpected inputs with default responses or clarifying questions. \n\n### 5. Continuous Improvement\n\n- **Monitor Performance**: Use analytics to track how well your agent understands users.\n- **Gather Feedback**: Implement mechanisms for users to report misunderstandings.\n- **Iterate Design**: Regularly update intents, entities, and responses based on insights.\n\n***\n\n## Conclusion\n\nUnderstanding user inputs is at the heart of effective conversational AI. By combining foundational elements like intents and entities with advanced technologies like LLMs and robust memory management, you can design AI agents that interact with users naturally and efficiently.\n\nRemember, the goal is to create an agent that doesn't just respond but understands鈥攃apturing the nuances of human language and providing meaningful, context-aware interactions. By prioritizing the understanding component of your agent design, you set the stage for high-performing, user-centric conversations.\n\n***\n\n## Additional Resources\n\n- **[Introduction to Intents](https://docs.voiceflow.com/docs/intents)**: Dive deeper into defining and using intents effectively in your conversational designs.\n- **[Introduction to Entities](https://docs.voiceflow.com/docs/entities)**: Dive deeper into defining and using intents effectively in your conversational designs.\n- **[LLM Intent Classification](https://docs.voiceflow.com/docs/llm-intent-classification-method)**: Learn how to implement and customize LLMs for enhanced intent recognition.\n- **[Memory Management in Voiceflow](https://docs.voiceflow.com/docs/memory)**: Explore how to use variables and context to maintain conversation flow.","opts":{"alwaysThrow":false,"compatibilityMode":false,"copyButtons":true,"correctnewlines":false,"markdownOptions":{"fences":true,"commonmark":true,"gfm":true,"ruleSpaces":false,"listItemIndent":"1","spacedTable":true,"paddedTable":true},"normalize":true,"lazyImages":true,"reusableContent":{"tags":{},"wrap":false},"safeMode":false,"settings":{"position":true},"theme":"light","resourceID":"66f5c749f9c4bb001fc0f0c3","resourceType":"page","baseUrl":"/","terms":[{"_id":"655fad103facf3004c288f20","term":"agent","definition":"Refers to one 'project', 'assistant', or 'bot' in your workspace"}],"variables":{"user":{},"defaults":[{"source":"security","_id":"652eaab69e0e88197bcf3b85","name":"authorization","type":"apiKey","apiSetting":"638914e738c0e800aac362b1"},{"source":"security","_id":"6541048b1a4e17099759a54d","name":"Authorization","type":"apiKey","apiSetting":"6202d5d4b858c90049050339"}]}},"terms":[{"_id":"655fad103facf3004c288f20","term":"agent","definition":"Refers to one 'project', 'assistant', or 'bot' in your workspace"}],"variables":{"user":{},"defaults":[{"source":"security","_id":"652eaab69e0e88197bcf3b85","name":"authorization","type":"apiKey","apiSetting":"638914e738c0e800aac362b1"},{"source":"security","_id":"6541048b1a4e17099759a54d","name":"Authorization","type":"apiKey","apiSetting":"6202d5d4b858c90049050339"}]},"mdx":false,"dehydrated":{"toc":"<nav><ul class=\"toc-list\"><li><a class=\"tocHeader\" href=\"#\"><i class=\"icon icon-text-align-left\"></i>Table of Contents</a></li><li class=\"toc-children\"><ul>\n<li>\n<a href=\"#introduction\">Introduction</a>\n</li>\n<li>\n<a href=\"#what-does-it-mean-for-an-ai-agent-to-understand-a-users-query\">What Does It Mean for an AI Agent to Understand a User&#x27;s Query?</a>\n</li>\n<li>\n<a href=\"#natural-language-understanding-nlu\">Natural Language Understanding (NLU)</a>\n<ul>\n<li><a href=\"#what-is-nlu\">What is NLU?</a></li>\n<li><a href=\"#key-components-of-nlu\">Key Components of NLU</a></li>\n<li><a href=\"#intents-the-building-blocks-of-understanding\">Intents: The Building Blocks of Understanding</a></li>\n</ul>\n</li>\n<li>\n<a href=\"#enhancing-understanding-with-large-language-models-llms\">Enhancing Understanding with Large Language Models (LLMs)</a>\n<ul>\n<li><a href=\"#what-are-llms\">What are LLMs?</a></li>\n<li><a href=\"#llms-in-intent-classification\">LLMs in Intent Classification</a></li>\n<li><a href=\"#hybrid-approach-combining-nlu-with-llms\">Hybrid Approach: Combining NLU with LLMs</a></li>\n</ul>\n</li>\n<li>\n<a href=\"#memory-and-context-in-conversations\">Memory and Context in Conversations</a>\n<ul>\n<li><a href=\"#the-role-of-memory\">The Role of Memory</a></li>\n<li><a href=\"#using-memory-to-enhance-understanding\">Using Memory to Enhance Understanding</a></li>\n<li><a href=\"#implementing-memory-in-voiceflow\">Implementing Memory in Voiceflow</a></li>\n</ul>\n</li>\n<li>\n<a href=\"#user-inputs-beyond-text\">User Inputs Beyond Text</a>\n<ul>\n<li><a href=\"#buttons-and-quick-replies\">Buttons and Quick Replies</a></li>\n<li><a href=\"#forms-and-structured-inputs\">Forms and Structured Inputs</a></li>\n</ul>\n</li>\n<li>\n<a href=\"#handling-no-reply-and-no-match-globally\">Handling No Reply and No Match Globally</a>\n<ul>\n<li><a href=\"#global-no-reply\">Global No Reply</a></li>\n<li><a href=\"#understanding-global-no-reply\">Understanding Global No Reply</a></li>\n<li><a href=\"#role-in-the-understanding-engine\">Role in the Understanding Engine</a></li>\n<li><a href=\"#global-no-match\">Global No Match</a></li>\n<li><a href=\"#understanding-global-no-match\">Understanding Global No Match</a></li>\n<li><a href=\"#role-in-the-understanding-engine-1\">Role in the Understanding Engine</a></li>\n<li><a href=\"#importance-in-agent-design\">Importance in Agent Design</a></li>\n</ul>\n</li>\n<li>\n<a href=\"#the-importance-of-a-good-understanding-model\">The Importance of a Good Understanding Model</a>\n<ul>\n<li><a href=\"#enhances-user-satisfaction\"><strong>Enhances User Satisfaction</strong></a></li>\n<li><a href=\"#builds-trust\"><strong>Builds Trust</strong></a></li>\n<li><a href=\"#improves-business-outcomes\"><strong>Improves Business Outcomes</strong></a></li>\n</ul>\n</li>\n<li>\n<a href=\"#utilizing-tools-and-techniques-for-better-understanding\">Utilizing Tools and Techniques for Better Understanding</a>\n<ul>\n<li><a href=\"#1-intents-and-entities\">1. Intents and Entities</a></li>\n<li><a href=\"#2-llm-based-intent-classification\">2. LLM-Based Intent Classification</a></li>\n<li><a href=\"#3-memory-management\">3. Memory Management</a></li>\n<li><a href=\"#4-user-input-methods\">4. User Input Methods</a></li>\n<li><a href=\"#5-continuous-improvement\">5. Continuous Improvement</a></li>\n</ul>\n</li>\n<li>\n<a href=\"#conclusion\">Conclusion</a>\n</li>\n<li>\n<a href=\"#additional-resources\">Additional Resources</a>\n</li>\n</ul></li></ul></nav>","body":"<h2 class=\"heading heading-2 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"introduction\"></div><div class=\"heading-text\"><div id=\"section-introduction\" class=\"heading-anchor_backwardsCompatibility\"></div>Introduction</div><a aria-label=\"Skip link to Introduction\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#introduction\"></a></h2>\n<p>In the rapidly evolving field of conversational AI, one of the most critical aspects is enabling AI agents to <strong>understand user inputs</strong> effectively. Whether it&#x27;s a simple greeting, a complex question, or selecting a button in a user interface, understanding the user&#x27;s intent is paramount for creating meaningful and efficient interactions.</p>\n<p>This guide provides a high-level overview of how AI agents interpret and process user inputs, especially natural language, to deliver appropriate responses. We&#x27;ll explore the fundamental concepts that empower AI agents to comprehend user queries, including <strong>Intents</strong>, <strong>Natural Language Understanding (NLU)</strong>, <strong>Large Language Models (LLMs)</strong>, <strong>Memory and Context</strong>, and various input methods like button selections.</p>\n<hr/>\n<h2 class=\"heading heading-2 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"what-does-it-mean-for-an-ai-agent-to-understand-a-users-query\"></div><div class=\"heading-text\"><div id=\"section-what-does-it-mean-for-an-ai-agent-to-understand-a-users-query\" class=\"heading-anchor_backwardsCompatibility\"></div>What Does It Mean for an AI Agent to Understand a User&#x27;s Query?</div><a aria-label=\"Skip link to What Does It Mean for an AI Agent to Understand a User&#x27;s Query?\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#what-does-it-mean-for-an-ai-agent-to-understand-a-users-query\"></a></h2>\n<p>Understanding a user&#x27;s query involves several layers of processing:</p>\n<ol>\n<li><strong>Capturing the Input</strong>: Receiving the user&#x27;s message, which could be text, voice, or a selection.</li>\n<li><strong>Interpreting the Input</strong>: Analyzing the input to determine what the user wants or needs.</li>\n<li><strong>Determining the Intent</strong>: Identifying the purpose behind the user&#x27;s message.</li>\n<li><strong>Extracting Relevant Information</strong>: Pulling out specific details or entities that are important for the response.</li>\n<li><strong>Generating an Appropriate Response</strong>: Crafting a reply that addresses the user&#x27;s intent accurately and helpfully.</li>\n</ol>\n<p>An AI agent&#x27;s ability to perform these steps effectively defines its <strong>understanding</strong> of the user. This process relies on various technologies and methodologies, which we&#x27;ll explore in the following sections.</p>\n<hr/>\n<h2 class=\"heading heading-2 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"natural-language-understanding-nlu\"></div><div class=\"heading-text\"><div id=\"section-natural-language-understanding-nlu\" class=\"heading-anchor_backwardsCompatibility\"></div>Natural Language Understanding (NLU)</div><a aria-label=\"Skip link to Natural Language Understanding (NLU)\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#natural-language-understanding-nlu\"></a></h2>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"what-is-nlu\"></div><div class=\"heading-text\"><div id=\"section-what-is-nlu\" class=\"heading-anchor_backwardsCompatibility\"></div>What is NLU?</div><a aria-label=\"Skip link to What is NLU?\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#what-is-nlu\"></a></h3>\n<p><strong>Natural Language Understanding (NLU)</strong> is a branch of artificial intelligence that focuses on machine reading comprehension. It enables AI agents to understand and interpret human language in a way that&#x27;s both meaningful and actionable.</p>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"key-components-of-nlu\"></div><div class=\"heading-text\"><div id=\"section-key-components-of-nlu\" class=\"heading-anchor_backwardsCompatibility\"></div>Key Components of NLU</div><a aria-label=\"Skip link to Key Components of NLU\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#key-components-of-nlu\"></a></h3>\n<ul>\n<li><strong>Intent Recognition</strong>: Determining the user&#x27;s goal or purpose.</li>\n<li><strong>Entity Extraction</strong>: Identifying and extracting specific pieces of information from the input (e.g., dates, names, locations).</li>\n<li><strong>Contextual Understanding</strong>: Interpreting the input based on the context of the conversation.</li>\n</ul>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"intents-the-building-blocks-of-understanding\"></div><div class=\"heading-text\"><div id=\"section-intents-the-building-blocks-of-understanding\" class=\"heading-anchor_backwardsCompatibility\"></div>Intents: The Building Blocks of Understanding</div><a aria-label=\"Skip link to Intents: The Building Blocks of Understanding\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#intents-the-building-blocks-of-understanding\"></a></h3>\n<p><strong>Intents</strong> represent the actions or goals that users have when interacting with an AI agent. They are crucial for guiding the agent&#x27;s responses.</p>\n<ul>\n<li><strong>Example Intents</strong>:\n<ul>\n<li><strong>Greeting Intent</strong>: When a user says &quot;Hello&quot; or &quot;Hi.&quot;</li>\n<li><strong>Booking Intent</strong>: When a user wants to book a service or appointment.</li>\n<li><strong>Support Intent</strong>: When a user needs help with a product or service.</li>\n</ul>\n</li>\n</ul>\n<p>While this guide doesn&#x27;t delve deeply into intents, it&#x27;s important to recognize them as foundational elements in designing conversational agents.</p>\n<hr/>\n<h2 class=\"heading heading-2 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"enhancing-understanding-with-large-language-models-llms\"></div><div class=\"heading-text\"><div id=\"section-enhancing-understanding-with-large-language-models-ll-ms\" class=\"heading-anchor_backwardsCompatibility\"></div>Enhancing Understanding with Large Language Models (LLMs)</div><a aria-label=\"Skip link to Enhancing Understanding with Large Language Models (LLMs)\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#enhancing-understanding-with-large-language-models-llms\"></a></h2>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"what-are-llms\"></div><div class=\"heading-text\"><div id=\"section-what-are-ll-ms\" class=\"heading-anchor_backwardsCompatibility\"></div>What are LLMs?</div><a aria-label=\"Skip link to What are LLMs?\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#what-are-llms\"></a></h3>\n<p><strong>Large Language Models (LLMs)</strong> are advanced AI models trained on vast amounts of textual data. They excel at understanding and generating human-like text, making them powerful tools for interpreting user inputs.</p>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"llms-in-intent-classification\"></div><div class=\"heading-text\"><div id=\"section-ll-ms-in-intent-classification\" class=\"heading-anchor_backwardsCompatibility\"></div>LLMs in Intent Classification</div><a aria-label=\"Skip link to LLMs in Intent Classification\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#llms-in-intent-classification\"></a></h3>\n<p>By leveraging LLMs, AI agents can achieve higher accuracy in intent recognition. LLMs bring:</p>\n<ul>\n<li><strong>Contextual Awareness</strong>: Understanding nuances and context beyond keyword matching.</li>\n<li><strong>Flexibility</strong>: Interpreting a wide variety of phrasings and expressions.</li>\n<li><strong>Reduced Training Data Requirements</strong>: Performing effectively with fewer example inputs.</li>\n</ul>\n<p><span aria-label=\"\" class=\"img lightbox closed\" role=\"button\" tabindex=\"0\"><span class=\"lightbox-inner\"><img alt=\"\" loading=\"lazy\" src=\"https://files.readme.io/570c079a030aaf268369a114bc80ef43a1f560116acc59a70e104e31cceaa730-image.png\" align=\"\" caption=\"\" height=\"auto\" title=\"\" width=\"auto\"/></span></span></p>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"hybrid-approach-combining-nlu-with-llms\"></div><div class=\"heading-text\"><div id=\"section-hybrid-approach-combining-nlu-with-ll-ms\" class=\"heading-anchor_backwardsCompatibility\"></div>Hybrid Approach: Combining NLU with LLMs</div><a aria-label=\"Skip link to Hybrid Approach: Combining NLU with LLMs\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#hybrid-approach-combining-nlu-with-llms\"></a></h3>\n<p>A hybrid methodology blends traditional NLU techniques with the capabilities of LLMs to create a more robust intent classification system. This approach offers:</p>\n<ul>\n<li><strong>Predictability</strong>: Reliability in recognizing known intents.</li>\n<li><strong>Adaptability</strong>: Ability to handle unexpected inputs gracefully.</li>\n<li><strong>Efficiency</strong>: Streamlined agent building with minimal training data.</li>\n</ul>\n<p>For a detailed exploration, refer to our guide on <a href=\"#\" target=\"\" title=\"\">LLM Intent Classification</a>.</p>\n<hr/>\n<h2 class=\"heading heading-2 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"memory-and-context-in-conversations\"></div><div class=\"heading-text\"><div id=\"section-memory-and-context-in-conversations\" class=\"heading-anchor_backwardsCompatibility\"></div>Memory and Context in Conversations</div><a aria-label=\"Skip link to Memory and Context in Conversations\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#memory-and-context-in-conversations\"></a></h2>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"the-role-of-memory\"></div><div class=\"heading-text\"><div id=\"section-the-role-of-memory\" class=\"heading-anchor_backwardsCompatibility\"></div>The Role of Memory</div><a aria-label=\"Skip link to The Role of Memory\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#the-role-of-memory\"></a></h3>\n<p><strong>Memory</strong> in conversational AI refers to the agent&#x27;s ability to retain information from previous interactions within a conversation. This includes:</p>\n<ul>\n<li><strong>User&#x27;s Past Inputs</strong>: What the user has said earlier in the conversation.</li>\n<li><strong>Agent&#x27;s Previous Responses</strong>: The replies given by the agent.</li>\n<li><strong>Stored Variables</strong>: Specific data points captured for later use (e.g., user preferences, account details).</li>\n</ul>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"using-memory-to-enhance-understanding\"></div><div class=\"heading-text\"><div id=\"section-using-memory-to-enhance-understanding\" class=\"heading-anchor_backwardsCompatibility\"></div>Using Memory to Enhance Understanding</div><a aria-label=\"Skip link to Using Memory to Enhance Understanding\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#using-memory-to-enhance-understanding\"></a></h3>\n<p>By maintaining context, AI agents can:</p>\n<ul>\n<li><strong>Provide Relevant Responses</strong>: Tailor replies based on prior information.</li>\n<li><strong>Disambiguate Intent</strong>: Use previous inputs to clarify ambiguous queries.</li>\n<li><strong>Improve User Experience</strong>: Create a seamless and coherent conversation flow.</li>\n</ul>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"implementing-memory-in-voiceflow\"></div><div class=\"heading-text\"><div id=\"section-implementing-memory-in-voiceflow\" class=\"heading-anchor_backwardsCompatibility\"></div>Implementing Memory in Voiceflow</div><a aria-label=\"Skip link to Implementing Memory in Voiceflow\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#implementing-memory-in-voiceflow\"></a></h3>\n<p>In Voiceflow, memory is managed through variables that store user inputs and other relevant data. These variables can be:</p>\n<ul>\n<li><strong>System Variables</strong>: Automatically maintained by the system (e.g., <button aria-label=\"Copy Code\" class=\"rdmd-code-copy fa\"></button><code class=\"rdmd-code lang- theme-light\" data-lang=\"\" name=\"\" tabindex=\"0\">{user.last_input}</code>).</li>\n<li><strong>Custom Variables</strong>: Defined by the designer to capture specific information.</li>\n</ul>\n<p>These variables can be passed as context to the AI when generating responses, ensuring that the conversation remains coherent and personalized.</p>\n<hr/>\n<h2 class=\"heading heading-2 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"user-inputs-beyond-text\"></div><div class=\"heading-text\"><div id=\"section-user-inputs-beyond-text\" class=\"heading-anchor_backwardsCompatibility\"></div>User Inputs Beyond Text</div><a aria-label=\"Skip link to User Inputs Beyond Text\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#user-inputs-beyond-text\"></a></h2>\n<p>While natural language is a primary mode of interaction, users can also communicate with AI agents through other means:</p>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"buttons-and-quick-replies\"></div><div class=\"heading-text\"><div id=\"section-buttons-and-quick-replies\" class=\"heading-anchor_backwardsCompatibility\"></div>Buttons and Quick Replies</div><a aria-label=\"Skip link to Buttons and Quick Replies\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#buttons-and-quick-replies\"></a></h3>\n<ul>\n<li><strong>Purpose</strong>: Provide users with predefined options to select from.</li>\n<li><strong>Benefits</strong>:\n<ul>\n<li><strong>Simplify Choices</strong>: Reduce the cognitive load on users by presenting clear options.</li>\n<li><strong>Improve Accuracy</strong>: Minimize misunderstandings by limiting inputs to expected responses.</li>\n</ul>\n</li>\n<li><strong>Implementation</strong>: Incorporate buttons or quick reply options in the conversation design for actions like confirming choices, selecting from a list, or navigating menus.</li>\n</ul>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"forms-and-structured-inputs\"></div><div class=\"heading-text\"><div id=\"section-forms-and-structured-inputs\" class=\"heading-anchor_backwardsCompatibility\"></div>Forms and Structured Inputs</div><a aria-label=\"Skip link to Forms and Structured Inputs\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#forms-and-structured-inputs\"></a></h3>\n<ul>\n<li><strong>Use Cases</strong>: Collecting specific information like contact details, feedback, or survey responses.</li>\n<li><strong>Advantages</strong>:\n<ul>\n<li><strong>Data Validation</strong>: Ensure inputs meet required formats (e.g., email addresses, phone numbers).</li>\n<li><strong>Efficiency</strong>: Streamline data collection in a user-friendly manner.</li>\n</ul>\n</li>\n</ul>\n<hr/>\n<h2 class=\"heading heading-2 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"handling-no-reply-and-no-match-globally\"></div><div class=\"heading-text\"><div id=\"section-handling-no-reply-and-no-match-globally\" class=\"heading-anchor_backwardsCompatibility\"></div>Handling No Reply and No Match Globally</div><a aria-label=\"Skip link to Handling No Reply and No Match Globally\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#handling-no-reply-and-no-match-globally\"></a></h2>\n<p>In conversational AI, it&#x27;s essential to manage situations where the user doesn&#x27;t respond or provides input that the agent doesn&#x27;t understand. Two key components for handling these scenarios are <strong>Global No Reply</strong> and <strong>Global No Match</strong>.</p>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"global-no-reply\"></div><div class=\"heading-text\"><div id=\"section-global-no-reply\" class=\"heading-anchor_backwardsCompatibility\"></div>Global No Reply</div><a aria-label=\"Skip link to Global No Reply\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#global-no-reply\"></a></h3>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"understanding-global-no-reply\"></div><div class=\"heading-text\"><div id=\"section-understanding-global-no-reply\" class=\"heading-anchor_backwardsCompatibility\"></div>Understanding Global No Reply</div><a aria-label=\"Skip link to Understanding Global No Reply\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#understanding-global-no-reply\"></a></h3>\n<p><strong>Global No Reply</strong> refers to how an AI agent handles situations where the user remains silent or inactive for a specified period. This could happen if the user steps away, gets distracted, or experiences technical issues.</p>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"role-in-the-understanding-engine\"></div><div class=\"heading-text\"><div id=\"section-role-in-the-understanding-engine\" class=\"heading-anchor_backwardsCompatibility\"></div>Role in the Understanding Engine</div><a aria-label=\"Skip link to Role in the Understanding Engine\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#role-in-the-understanding-engine\"></a></h3>\n<ul>\n<li><strong>Maintaining Engagement</strong>: By detecting user inactivity, the agent can decide whether to prompt the user again, wait longer, or end the conversation politely.</li>\n<li><strong>User Experience</strong>: A thoughtful response to no reply scenarios ensures users don&#x27;t feel ignored or confused about what to do next.</li>\n<li><strong>Context Awareness</strong>: The agent considers the conversation&#x27;s state to provide an appropriate reaction to silence, maintaining the conversation&#x27;s flow.</li>\n</ul>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"global-no-match\"></div><div class=\"heading-text\"><div id=\"section-global-no-match\" class=\"heading-anchor_backwardsCompatibility\"></div>Global No Match</div><a aria-label=\"Skip link to Global No Match\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#global-no-match\"></a></h3>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"understanding-global-no-match\"></div><div class=\"heading-text\"><div id=\"section-understanding-global-no-match\" class=\"heading-anchor_backwardsCompatibility\"></div>Understanding Global No Match</div><a aria-label=\"Skip link to Understanding Global No Match\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#understanding-global-no-match\"></a></h3>\n<p><strong>Global No Match</strong> addresses situations where the user&#x27;s input doesn&#x27;t match any of the defined intents or expected responses. It acts as a safety net, ensuring that the agent can handle unexpected or unrecognized inputs gracefully.</p>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"role-in-the-understanding-engine-1\"></div><div class=\"heading-text\"><div id=\"section-role-in-the-understanding-engine\" class=\"heading-anchor_backwardsCompatibility\"></div>Role in the Understanding Engine</div><a aria-label=\"Skip link to Role in the Understanding Engine\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#role-in-the-understanding-engine-1\"></a></h3>\n<ul>\n<li><strong>Fallback Mechanism</strong>: Provides a default response or action when the agent cannot interpret the user&#x27;s input.</li>\n<li><strong>User Guidance</strong>: Helps redirect the user back to a known conversation path or offers assistance.</li>\n<li><strong>Error Handling</strong>: Prevents the conversation from stalling or ending abruptly due to misunderstandings.</li>\n</ul>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"importance-in-agent-design\"></div><div class=\"heading-text\"><div id=\"section-importance-in-agent-design\" class=\"heading-anchor_backwardsCompatibility\"></div>Importance in Agent Design</div><a aria-label=\"Skip link to Importance in Agent Design\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#importance-in-agent-design\"></a></h3>\n<ul>\n<li><strong>Consistency</strong>: Global No Reply and Global No Match ensure that unhandled scenarios are managed uniformly across the entire conversation.</li>\n<li><strong>Resilience</strong>: They enhance the agent&#x27;s robustness, allowing it to handle a wide range of user behaviors and inputs.</li>\n<li><strong>Improved User Satisfaction</strong>: By thoughtfully managing these situations, users are less likely to feel frustrated or abandoned, leading to a better overall experience.</li>\n</ul>\n<hr/>\n<h2 class=\"heading heading-2 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"the-importance-of-a-good-understanding-model\"></div><div class=\"heading-text\"><div id=\"section-the-importance-of-a-good-understanding-model\" class=\"heading-anchor_backwardsCompatibility\"></div>The Importance of a Good Understanding Model</div><a aria-label=\"Skip link to The Importance of a Good Understanding Model\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#the-importance-of-a-good-understanding-model\"></a></h2>\n<p>A high-performing understanding model is critical for several reasons:</p>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"enhances-user-satisfaction\"></div><div class=\"heading-text\"><div id=\"section-enhances-user-satisfaction\" class=\"heading-anchor_backwardsCompatibility\"></div><strong>Enhances User Satisfaction</strong></div><a aria-label=\"Skip link to [object Object]\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#enhances-user-satisfaction\"></a></h3>\n<ul>\n<li><strong>Accuracy</strong>: Correctly interpreting user inputs leads to appropriate responses.</li>\n<li><strong>Efficiency</strong>: Reduces the need for repeated clarifications, saving time for the user.</li>\n</ul>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"builds-trust\"></div><div class=\"heading-text\"><div id=\"section-builds-trust\" class=\"heading-anchor_backwardsCompatibility\"></div><strong>Builds Trust</strong></div><a aria-label=\"Skip link to [object Object]\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#builds-trust\"></a></h3>\n<ul>\n<li><strong>Reliability</strong>: Consistent understanding fosters user confidence in the AI agent.</li>\n<li><strong>Personalization</strong>: Remembering past interactions creates a more personalized experience.</li>\n</ul>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"improves-business-outcomes\"></div><div class=\"heading-text\"><div id=\"section-improves-business-outcomes\" class=\"heading-anchor_backwardsCompatibility\"></div><strong>Improves Business Outcomes</strong></div><a aria-label=\"Skip link to [object Object]\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#improves-business-outcomes\"></a></h3>\n<ul>\n<li><strong>Goal Achievement</strong>: Efficiently guiding users to complete tasks (e.g., making a purchase, resolving an issue).</li>\n<li><strong>Data Collection</strong>: Accurately capturing information that can inform business decisions.</li>\n</ul>\n<hr/>\n<h2 class=\"heading heading-2 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"utilizing-tools-and-techniques-for-better-understanding\"></div><div class=\"heading-text\"><div id=\"section-utilizing-tools-and-techniques-for-better-understanding\" class=\"heading-anchor_backwardsCompatibility\"></div>Utilizing Tools and Techniques for Better Understanding</div><a aria-label=\"Skip link to Utilizing Tools and Techniques for Better Understanding\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#utilizing-tools-and-techniques-for-better-understanding\"></a></h2>\n<p>To develop a high-performing understanding component in your AI agent, consider leveraging the following tools and techniques:</p>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"1-intents-and-entities\"></div><div class=\"heading-text\"><div id=\"section-1-intents-and-entities\" class=\"heading-anchor_backwardsCompatibility\"></div>1. Intents and Entities</div><a aria-label=\"Skip link to 1. Intents and Entities\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#1-intents-and-entities\"></a></h3>\n<ul>\n<li><strong>Define Clear Intents</strong>: Map out the primary purposes users have when interacting with your agent.</li>\n<li><strong>Identify Entities</strong>: Determine the specific pieces of information needed to fulfill each intent.</li>\n</ul>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"2-llm-based-intent-classification\"></div><div class=\"heading-text\"><div id=\"section-2-llm-based-intent-classification\" class=\"heading-anchor_backwardsCompatibility\"></div>2. LLM-Based Intent Classification</div><a aria-label=\"Skip link to 2. LLM-Based Intent Classification\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#2-llm-based-intent-classification\"></a></h3>\n<ul>\n<li><strong>Implement LLMs</strong>: Use large language models to enhance intent recognition accuracy.</li>\n<li><strong>Hybrid Models</strong>: Combine traditional NLU with LLMs for a balanced approach.</li>\n<li><strong>Customization</strong>: Tailor the LLM&#x27;s understanding through intent descriptions and example utterances.</li>\n</ul>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"3-memory-management\"></div><div class=\"heading-text\"><div id=\"section-3-memory-management\" class=\"heading-anchor_backwardsCompatibility\"></div>3. Memory Management</div><a aria-label=\"Skip link to 3. Memory Management\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#3-memory-management\"></a></h3>\n<ul>\n<li><strong>Use Variables</strong>: Store important information for use throughout the conversation.</li>\n<li><strong>Contextual Responses</strong>: Reference past inputs to provide coherent and relevant replies.</li>\n<li><strong>Persistent Memory</strong>: Consider what information should persist beyond a single session for returning users.</li>\n</ul>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"4-user-input-methods\"></div><div class=\"heading-text\"><div id=\"section-4-user-input-methods\" class=\"heading-anchor_backwardsCompatibility\"></div>4. User Input Methods</div><a aria-label=\"Skip link to 4. User Input Methods\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#4-user-input-methods\"></a></h3>\n<ul>\n<li><strong>Buttons</strong>: Simplify interactions and reduce errors.</li>\n<li><strong>Fallback Mechanisms</strong>: Prepare for unexpected inputs with default responses or clarifying questions. </li>\n</ul>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"5-continuous-improvement\"></div><div class=\"heading-text\"><div id=\"section-5-continuous-improvement\" class=\"heading-anchor_backwardsCompatibility\"></div>5. Continuous Improvement</div><a aria-label=\"Skip link to 5. Continuous Improvement\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#5-continuous-improvement\"></a></h3>\n<ul>\n<li><strong>Monitor Performance</strong>: Use analytics to track how well your agent understands users.</li>\n<li><strong>Gather Feedback</strong>: Implement mechanisms for users to report misunderstandings.</li>\n<li><strong>Iterate Design</strong>: Regularly update intents, entities, and responses based on insights.</li>\n</ul>\n<hr/>\n<h2 class=\"heading heading-2 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"conclusion\"></div><div class=\"heading-text\"><div id=\"section-conclusion\" class=\"heading-anchor_backwardsCompatibility\"></div>Conclusion</div><a aria-label=\"Skip link to Conclusion\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#conclusion\"></a></h2>\n<p>Understanding user inputs is at the heart of effective conversational AI. By combining foundational elements like intents and entities with advanced technologies like LLMs and robust memory management, you can design AI agents that interact with users naturally and efficiently.</p>\n<p>Remember, the goal is to create an agent that doesn&#x27;t just respond but understands鈥攃apturing the nuances of human language and providing meaningful, context-aware interactions. By prioritizing the understanding component of your agent design, you set the stage for high-performing, user-centric conversations.</p>\n<hr/>\n<h2 class=\"heading heading-2 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"additional-resources\"></div><div class=\"heading-text\"><div id=\"section-additional-resources\" class=\"heading-anchor_backwardsCompatibility\"></div>Additional Resources</div><a aria-label=\"Skip link to Additional Resources\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#additional-resources\"></a></h2>\n<ul>\n<li><strong><a href=\"https://docs.voiceflow.com/docs/intents\" target=\"\" title=\"\">Introduction to Intents</a></strong>: Dive deeper into defining and using intents effectively in your conversational designs.</li>\n<li><strong><a href=\"https://docs.voiceflow.com/docs/entities\" target=\"\" title=\"\">Introduction to Entities</a></strong>: Dive deeper into defining and using intents effectively in your conversational designs.</li>\n<li><strong><a href=\"https://docs.voiceflow.com/docs/llm-intent-classification-method\" target=\"\" title=\"\">LLM Intent Classification</a></strong>: Learn how to implement and customize LLMs for enhanced intent recognition.</li>\n<li><strong><a href=\"https://docs.voiceflow.com/docs/memory\" target=\"\" title=\"\">Memory Management in Voiceflow</a></strong>: Explore how to use variables and context to maintain conversation flow.</li>\n</ul>"}},"doc":{"metadata":{"image":[],"title":"","description":"","keywords":"","robots":"index"},"mdx":{"altBody":"","status":"rdmd"},"api":{"method":"get","url":"","auth":"required","params":[]},"next":{"description":"","pages":[]},"algolia":{"recordCount":37,"publishPending":false,"translationFailure":false,"updatedAt":"2024-10-01T19:22:10.078Z"},"title":"Understanding: How AI Agents Understand User Inputs","icon":"","updates":[],"type":"basic","slug":"listen-fundamentals","excerpt":"","body":"## Introduction\n\nIn the rapidly evolving field of conversational AI, one of the most critical aspects is enabling AI agents to **understand user inputs** effectively. Whether it's a simple greeting, a complex question, or selecting a button in a user interface, understanding the user's intent is paramount for creating meaningful and efficient interactions.\n\nThis guide provides a high-level overview of how AI agents interpret and process user inputs, especially natural language, to deliver appropriate responses. We'll explore the fundamental concepts that empower AI agents to comprehend user queries, including **Intents**, **Natural Language Understanding (NLU)**, **Large Language Models (LLMs)**, **Memory and Context**, and various input methods like button selections.\n\n***\n\n## What Does It Mean for an AI Agent to Understand a User's Query?\n\nUnderstanding a user's query involves several layers of processing:\n\n1. **Capturing the Input**: Receiving the user's message, which could be text, voice, or a selection.\n2. **Interpreting the Input**: Analyzing the input to determine what the user wants or needs.\n3. **Determining the Intent**: Identifying the purpose behind the user's message.\n4. **Extracting Relevant Information**: Pulling out specific details or entities that are important for the response.\n5. **Generating an Appropriate Response**: Crafting a reply that addresses the user's intent accurately and helpfully.\n\nAn AI agent's ability to perform these steps effectively defines its **understanding** of the user. This process relies on various technologies and methodologies, which we'll explore in the following sections.\n\n***\n\n## Natural Language Understanding (NLU)\n\n### What is NLU?\n\n**Natural Language Understanding (NLU)** is a branch of artificial intelligence that focuses on machine reading comprehension. It enables AI agents to understand and interpret human language in a way that's both meaningful and actionable.\n\n### Key Components of NLU\n\n- **Intent Recognition**: Determining the user's goal or purpose.\n- **Entity Extraction**: Identifying and extracting specific pieces of information from the input (e.g., dates, names, locations).\n- **Contextual Understanding**: Interpreting the input based on the context of the conversation.\n\n### Intents: The Building Blocks of Understanding\n\n**Intents** represent the actions or goals that users have when interacting with an AI agent. They are crucial for guiding the agent's responses.\n\n- **Example Intents**:\n - **Greeting Intent**: When a user says \"Hello\" or \"Hi.\"\n - **Booking Intent**: When a user wants to book a service or appointment.\n - **Support Intent**: When a user needs help with a product or service.\n\nWhile this guide doesn't delve deeply into intents, it's important to recognize them as foundational elements in designing conversational agents.\n\n***\n\n## Enhancing Understanding with Large Language Models (LLMs)\n\n### What are LLMs?\n\n**Large Language Models (LLMs)** are advanced AI models trained on vast amounts of textual data. They excel at understanding and generating human-like text, making them powerful tools for interpreting user inputs.\n\n### LLMs in Intent Classification\n\nBy leveraging LLMs, AI agents can achieve higher accuracy in intent recognition. LLMs bring:\n\n- **Contextual Awareness**: Understanding nuances and context beyond keyword matching.\n- **Flexibility**: Interpreting a wide variety of phrasings and expressions.\n- **Reduced Training Data Requirements**: Performing effectively with fewer example inputs.\n\n![](https://files.readme.io/570c079a030aaf268369a114bc80ef43a1f560116acc59a70e104e31cceaa730-image.png)\n\n### Hybrid Approach: Combining NLU with LLMs\n\nA hybrid methodology blends traditional NLU techniques with the capabilities of LLMs to create a more robust intent classification system. This approach offers:\n\n- **Predictability**: Reliability in recognizing known intents.\n- **Adaptability**: Ability to handle unexpected inputs gracefully.\n- **Efficiency**: Streamlined agent building with minimal training data.\n\nFor a detailed exploration, refer to our guide on [LLM Intent Classification](#).\n\n***\n\n## Memory and Context in Conversations\n\n### The Role of Memory\n\n**Memory** in conversational AI refers to the agent's ability to retain information from previous interactions within a conversation. This includes:\n\n- **User's Past Inputs**: What the user has said earlier in the conversation.\n- **Agent's Previous Responses**: The replies given by the agent.\n- **Stored Variables**: Specific data points captured for later use (e.g., user preferences, account details).\n\n### Using Memory to Enhance Understanding\n\nBy maintaining context, AI agents can:\n\n- **Provide Relevant Responses**: Tailor replies based on prior information.\n- **Disambiguate Intent**: Use previous inputs to clarify ambiguous queries.\n- **Improve User Experience**: Create a seamless and coherent conversation flow.\n\n### Implementing Memory in Voiceflow\n\nIn Voiceflow, memory is managed through variables that store user inputs and other relevant data. These variables can be:\n\n- **System Variables**: Automatically maintained by the system (e.g., `{user.last_input}`).\n- **Custom Variables**: Defined by the designer to capture specific information.\n\nThese variables can be passed as context to the AI when generating responses, ensuring that the conversation remains coherent and personalized.\n\n***\n\n## User Inputs Beyond Text\n\nWhile natural language is a primary mode of interaction, users can also communicate with AI agents through other means:\n\n### Buttons and Quick Replies\n\n- **Purpose**: Provide users with predefined options to select from.\n- **Benefits**:\n - **Simplify Choices**: Reduce the cognitive load on users by presenting clear options.\n - **Improve Accuracy**: Minimize misunderstandings by limiting inputs to expected responses.\n- **Implementation**: Incorporate buttons or quick reply options in the conversation design for actions like confirming choices, selecting from a list, or navigating menus.\n\n### Forms and Structured Inputs\n\n- **Use Cases**: Collecting specific information like contact details, feedback, or survey responses.\n- **Advantages**:\n - **Data Validation**: Ensure inputs meet required formats (e.g., email addresses, phone numbers).\n - **Efficiency**: Streamline data collection in a user-friendly manner.\n\n***\n\n## Handling No Reply and No Match Globally\n\nIn conversational AI, it's essential to manage situations where the user doesn't respond or provides input that the agent doesn't understand. Two key components for handling these scenarios are **Global No Reply** and **Global No Match**.\n\n### Global No Reply\n\n### Understanding Global No Reply\n\n**Global No Reply** refers to how an AI agent handles situations where the user remains silent or inactive for a specified period. This could happen if the user steps away, gets distracted, or experiences technical issues.\n\n### Role in the Understanding Engine\n\n- **Maintaining Engagement**: By detecting user inactivity, the agent can decide whether to prompt the user again, wait longer, or end the conversation politely.\n- **User Experience**: A thoughtful response to no reply scenarios ensures users don't feel ignored or confused about what to do next.\n- **Context Awareness**: The agent considers the conversation's state to provide an appropriate reaction to silence, maintaining the conversation's flow.\n\n### Global No Match\n\n### Understanding Global No Match\n\n**Global No Match** addresses situations where the user's input doesn't match any of the defined intents or expected responses. It acts as a safety net, ensuring that the agent can handle unexpected or unrecognized inputs gracefully.\n\n### Role in the Understanding Engine\n\n- **Fallback Mechanism**: Provides a default response or action when the agent cannot interpret the user's input.\n- **User Guidance**: Helps redirect the user back to a known conversation path or offers assistance.\n- **Error Handling**: Prevents the conversation from stalling or ending abruptly due to misunderstandings.\n\n### Importance in Agent Design\n\n- **Consistency**: Global No Reply and Global No Match ensure that unhandled scenarios are managed uniformly across the entire conversation.\n- **Resilience**: They enhance the agent's robustness, allowing it to handle a wide range of user behaviors and inputs.\n- **Improved User Satisfaction**: By thoughtfully managing these situations, users are less likely to feel frustrated or abandoned, leading to a better overall experience.\n\n***\n\n## The Importance of a Good Understanding Model\n\nA high-performing understanding model is critical for several reasons:\n\n### **Enhances User Satisfaction**\n\n- **Accuracy**: Correctly interpreting user inputs leads to appropriate responses.\n- **Efficiency**: Reduces the need for repeated clarifications, saving time for the user.\n\n### **Builds Trust**\n\n- **Reliability**: Consistent understanding fosters user confidence in the AI agent.\n- **Personalization**: Remembering past interactions creates a more personalized experience.\n\n### **Improves Business Outcomes**\n\n- **Goal Achievement**: Efficiently guiding users to complete tasks (e.g., making a purchase, resolving an issue).\n- **Data Collection**: Accurately capturing information that can inform business decisions.\n\n***\n\n## Utilizing Tools and Techniques for Better Understanding\n\nTo develop a high-performing understanding component in your AI agent, consider leveraging the following tools and techniques:\n\n### 1. Intents and Entities\n\n- **Define Clear Intents**: Map out the primary purposes users have when interacting with your agent.\n- **Identify Entities**: Determine the specific pieces of information needed to fulfill each intent.\n\n### 2. LLM-Based Intent Classification\n\n- **Implement LLMs**: Use large language models to enhance intent recognition accuracy.\n- **Hybrid Models**: Combine traditional NLU with LLMs for a balanced approach.\n- **Customization**: Tailor the LLM's understanding through intent descriptions and example utterances.\n\n### 3. Memory Management\n\n- **Use Variables**: Store important information for use throughout the conversation.\n- **Contextual Responses**: Reference past inputs to provide coherent and relevant replies.\n- **Persistent Memory**: Consider what information should persist beyond a single session for returning users.\n\n### 4. User Input Methods\n\n- **Buttons**: Simplify interactions and reduce errors.\n- **Fallback Mechanisms**: Prepare for unexpected inputs with default responses or clarifying questions. \n\n### 5. Continuous Improvement\n\n- **Monitor Performance**: Use analytics to track how well your agent understands users.\n- **Gather Feedback**: Implement mechanisms for users to report misunderstandings.\n- **Iterate Design**: Regularly update intents, entities, and responses based on insights.\n\n***\n\n## Conclusion\n\nUnderstanding user inputs is at the heart of effective conversational AI. By combining foundational elements like intents and entities with advanced technologies like LLMs and robust memory management, you can design AI agents that interact with users naturally and efficiently.\n\nRemember, the goal is to create an agent that doesn't just respond but understands鈥攃apturing the nuances of human language and providing meaningful, context-aware interactions. By prioritizing the understanding component of your agent design, you set the stage for high-performing, user-centric conversations.\n\n***\n\n## Additional Resources\n\n- **[Introduction to Intents](https://docs.voiceflow.com/docs/intents)**: Dive deeper into defining and using intents effectively in your conversational designs.\n- **[Introduction to Entities](https://docs.voiceflow.com/docs/entities)**: Dive deeper into defining and using intents effectively in your conversational designs.\n- **[LLM Intent Classification](https://docs.voiceflow.com/docs/llm-intent-classification-method)**: Learn how to implement and customize LLMs for enhanced intent recognition.\n- **[Memory Management in Voiceflow](https://docs.voiceflow.com/docs/memory)**: Explore how to use variables and context to maintain conversation 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