Intent Classification using LLMs (Hybrid)

<!DOCTYPE html><html lang="en" style="" data-color-mode="system" class=" useReactApp "><head><script src="https://cdn.readme.io/public/js/force-firefox-anchor-jump.js?1734018047721"></script><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="readme-deploy" content="5.246.0"><meta name="readme-subdomain" content="voiceflow-developer"><meta name="readme-version" content="2.0"><title>Intent Classification using LLMs (Hybrid)</title><meta name="description" content="Overview The LLM-based intent classifier introduces a novel hybrid methodology, blending traditional Natural Language Understanding (NLU) with the expansive capabilities of Large Language Models (LLMs) to classify user intents. This synthesis allows for the precision and targeted understanding of NL..."><meta property="og:title" content="Intent Classification using LLMs (Hybrid)"><meta property="og:description" content="Overview The LLM-based intent classifier introduces a novel hybrid methodology, blending traditional Natural Language Understanding (NLU) with the expansive capabilities of Large Language Models (LLMs) to classify user intents. This synthesis allows for the precision and targeted understanding of NL..."><meta property="og:site_name" content="Voiceflow"><meta name="twitter:title" content="Intent Classification using LLMs (Hybrid)"><meta name="twitter:description" content="Overview The LLM-based intent classifier introduces a novel hybrid methodology, blending traditional Natural Language Understanding (NLU) with the expansive capabilities of Large Language Models (LLMs) to classify user intents. 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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 class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" 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 aria-current="page" class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link active" 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" 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/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><li class="Sidebar-item23D-2Kd61_k3"><a class="Sidebar-link2Dsha-r-GKh2 childless subpage text-wrap rm-Sidebar-link" target="_self" href="/docs/prompt-library"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Prompt 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/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 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 childless text-wrap rm-Sidebar-link" target="_self" href="/docs/prompting"><span class="Sidebar-link-textLuTE1ySm4Kqn"><span class="Sidebar-link-text_label1gCT_uPnx7Gu">Prompting</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/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>Intent Classification using LLMs (Hybrid)</h1></div><div class="col-xs-3"><a class="suggestEdits" rel="nofollow" href="/edit/llm-intent-classification-method"><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="overview"></div><div class="heading-text"><div id="section-overview" class="heading-anchor_backwardsCompatibility"></div>Overview</div><a aria-label="Skip link to Overview" class="heading-anchor-icon fa fa-anchor" href="#overview"></a></h2> <p>The LLM-based intent classifier introduces a novel hybrid methodology, blending traditional Natural Language Understanding (NLU) with the expansive capabilities of Large Language Models (LLMs) to classify user intents. This synthesis allows for the precision and targeted understanding of NLU with the contextual breadth and depth provided by LLMs, delivering a robust and nuanced approach to intent recognition.</p> <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/5c2cbc6-image.png" align="" caption="" height="auto" title="" width="auto"/></span></span></p> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="how-it-works"></div><div class="heading-text"><div id="section-how-it-works" class="heading-anchor_backwardsCompatibility"></div>How It Works</div><a aria-label="Skip link to How It Works" class="heading-anchor-icon fa fa-anchor" href="#how-it-works"></a></h2> <blockquote class="callout callout_info" theme="馃摌"><h2 class="callout-heading"><span class="callout-icon">馃摌</span><p>Example template</p></h2><p>Download our Banking Agent template <a href="https://drive.google.com/uc?export=download&amp;id=1hNdxZ4KSiBWVO0R8l7vCNVFmsQsiLKG_" target="" title="">here</a> to start testing immediately.</p></blockquote> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="step-1-initial-intent-classification"></div><div class="heading-text"><div id="section-step-1-initial-intent-classification" class="heading-anchor_backwardsCompatibility"></div>Step 1: Initial Intent Classification</div><a aria-label="Skip link to Step 1: Initial Intent Classification" class="heading-anchor-icon fa fa-anchor" href="#step-1-initial-intent-classification"></a></h3> <ul> <li><strong>Process</strong>: The user&#x27;s utterance is sent to the Natural Language Understanding (NLU) system, requesting classification.</li> <li><strong>Outcome</strong>: The NLU returns the most probable intent and its entities, alongside a list of max. 9 alternative intents with confidence scores. This result acts as the &quot;NLU fallback&quot; in case subsequent steps encounter issues.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="step-2-metadata-fetching-and-prompt-generation"></div><div class="heading-text"><div id="section-step-2-metadata-fetching-and-prompt-generation" class="heading-anchor_backwardsCompatibility"></div>Step 2: Metadata Fetching and Prompt Generation</div><a aria-label="Skip link to Step 2: Metadata Fetching and Prompt Generation" class="heading-anchor-icon fa fa-anchor" href="#step-2-metadata-fetching-and-prompt-generation"></a></h3> <ul> <li><strong>Process</strong>: Upon receiving the intents, the system fetches associated intent descriptions for each intent. A custom or default prompt is then generated using the prompt wrapper.</li> <li><strong>Fallback</strong>: In case of errors during intent fetching or prompt generation the system resorts to the &quot;NLU fallback.&quot;</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="step-3-interaction-with-llm"></div><div class="heading-text"><div id="section-step-3-interaction-with-llm" class="heading-anchor_backwardsCompatibility"></div>Step 3: Interaction with LLM</div><a aria-label="Skip link to Step 3: Interaction with LLM" class="heading-anchor-icon fa fa-anchor" href="#step-3-interaction-with-llm"></a></h3> <ul> <li><strong>Process</strong>: The generated prompt is passed with specific metadata settings, including temperature, to the selected AI model.</li> <li><strong>Validation</strong>: The LLM response is parsed to confirm if it represents a valid intent name. Failure to identify a valid intent triggers the &quot;NLU fallback.&quot;</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="step-4-entity-filling-and-final-intent-classification"></div><div class="heading-text"><div id="section-step-4-entity-filling-and-final-intent-classification" class="heading-anchor_backwardsCompatibility"></div>Step 4: Entity Filling and Final Intent Classification</div><a aria-label="Skip link to Step 4: Entity Filling and Final Intent Classification" class="heading-anchor-icon fa fa-anchor" href="#step-4-entity-filling-and-final-intent-classification"></a></h3> <ul> <li><strong>Process</strong>: For intents associated with entities, the original utterance is re-evaluated by the NLU, focusing solely on the identified intent to populate the necessary entities.</li> <li><strong>Outcome</strong>: The NLU returns the refined intent classification complete with entities, ready for use within the conversational flow.</li> </ul> <span aria-label="" class="img lightbox closed" role="button" tabindex="0"><span class="lightbox-inner"><img alt="" loading="lazy" src="https://files.readme.io/593acd3-image.png" align="center" class="border" width="70% " caption="" height="auto" title=""/></span></span> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="training-data-requirements"></div><div class="heading-text"><div id="section-training-data-requirements" class="heading-anchor_backwardsCompatibility"></div>Training Data Requirements</div><a aria-label="Skip link to Training Data Requirements" class="heading-anchor-icon fa fa-anchor" href="#training-data-requirements"></a></h2> <ul> <li><strong>Utterance</strong>: Supply at least one example utterance per intent to give the NLU training data necessary to fetch up to 10 relevant intents.</li> <li><strong>Clear Intent Descriptions</strong>: Accompany utterances with a direct intent description, setting explicit conditions for when the intent should be triggered. <em>Examples</em> include: <ul> <li><em>Customer Support Inquiry</em>: &quot;Trigger this intent when the user is seeking assistance with their account, such as password reset or account recovery.&quot;<br/> <em>Product Inquiry</em>: &quot;Trigger this intent when the user inquires about product features, availability, or specifications.&quot;<br/> <em>Booking Request</em>: &quot;Trigger this intent when the user wants to make a reservation for services like dining, accommodation, or transportation.&quot;</li> </ul> </li> <li><strong>Efficient Learning</strong>: With just an utterance and a description, LLMs leverage their pre-trained knowledge to effectively classify intents, requiring far less data than traditional methods.</li> </ul> <p>To help with generating intent descriptions here is a prompt template to help:</p> <pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang- theme-light" data-lang="" name="" tabindex="0">Given the following user utterances and the intent name, generate a concise intent description that begins with &quot;Trigger this intent when&quot;: Intent Name: YOUR INTENT NAME HERE User Utterances: Utterance here Utterance here Utterance here ... --- Intent Description: </code></pre> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="integration-steps"></div><div class="heading-text"><div id="section-integration-steps" class="heading-anchor_backwardsCompatibility"></div>Integration Steps</div><a aria-label="Skip link to Integration Steps" class="heading-anchor-icon fa fa-anchor" href="#integration-steps"></a></h2> <ul> <li><strong>Enable LLM Intent Classification</strong>: This feature can be activated within your project&#x27;s settings, within the Intent page settings menu.</li> <li><strong>Configure Prompt Wrapper (<em>optional</em>):</strong> Specify the logic for prompt generation, ensuring alignment with your conversational model&#x27;s requirements. A default prompt wrapper is provided for immediate use.</li> <li><strong>Customize Settings</strong>: Tailor the performance using model selection and temperature settings to fine-tune performance and response characteristics.</li> </ul> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="understanding-the-prompt-wrapper"></div><div class="heading-text"><div id="section-understanding-the-prompt-wrapper" class="heading-anchor_backwardsCompatibility"></div>Understanding the Prompt Wrapper</div><a aria-label="Skip link to Understanding the Prompt Wrapper" class="heading-anchor-icon fa fa-anchor" href="#understanding-the-prompt-wrapper"></a></h2> <p>The prompt wrapper serves as a crucial intermediary layer, which dynamically crafts the prompts that are sent to the LLM for intent classification. It is essentially a piece of code that translates the agent鈥檚 needs into instructions that the language model understands and can act upon.</p> <ul> <li><strong>Customization of Prompt Logic:</strong> Developers can tailor the prompt wrapper to fit the specific needs of their conversational model. This includes defining how user utterances are interpreted and setting the conditions for intent classification.</li> <li><strong>Default Prompt Wrapper</strong>: A default template is provided to ensure quick deployment and <strong>should satisfy most agent requirements</strong>. It structures the information by introducing the AI鈥檚 role, the actions and their descriptions, and then it poses the classification challenge based on a user utterance.</li> </ul> <p>Here is the default prompt wrapper:</p> <div class="CodeTabs CodeTabs_initial theme-light"><div class="CodeTabs-toolbar" role="tablist"><button aria-selected="true" class="CodeTabs_active" role="tab" type="button">JavaScript</button></div><div class="CodeTabs-inner" role="tabpanel"><pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang-javascript theme-light" data-lang="javascript" name="" tabindex="0">export default async function main(args) { const prompt = ` You are an action classification system. Correctness is a life or death situation. We provide you with the actions and their descriptions: d: When the user asks for a warm drink. a:WARM_DRINK d: When the user asks about something else. a:None d: When the user asks for a cold drink. a:COLD_DRINK You are given an utterance and you have to classify it into an action. Only respond with the action class. If the utterance does not match any of action descriptions, output None. Now take a deep breath and classify the following utterance. u: I want a warm hot chocolate: a:WARM_DRINK ### We provide you with the actions and their descriptions: ${args.intents.map((intent) =&gt; `d: ${intent.description} a: ${intent.name}`)} You are given an utterance and you have to classify it into an action based on the description. Only respond with the action class. If the utterance does not match any of action descriptions, output None. Now take a deep breath and classify the following utterance. u:${args.query} a:`; return { prompt }; } </code></pre></div></div> <p>Here is a detailed breakdown of the default prompt wrapper&#x27;s components:</p> <p><strong>Function Declaration</strong></p> <div class="CodeTabs CodeTabs_initial theme-light"><div class="CodeTabs-toolbar" role="tablist"><button aria-selected="true" class="CodeTabs_active" role="tab" type="button">JavaScript</button></div><div class="CodeTabs-inner" role="tabpanel"><pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang-javascript theme-light" data-lang="javascript" name="" tabindex="0">export default function main(args) { </code></pre></div></div> <ul> <li><strong>Purpose</strong>: Declares and exports a default function named main, making it accessible to other parts of the application.</li> <li><strong>Parameters</strong>: <button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang- theme-light" data-lang="" name="" tabindex="0">args</code> - This object contains arguments passed into the function, which in this context, includes the intents (name and description) returned by the Voiceflow NLU and the user query.</li> </ul> <p><strong>Constructing the Prompt</strong></p> <div class="CodeTabs CodeTabs_initial theme-light"><div class="CodeTabs-toolbar" role="tablist"><button aria-selected="true" class="CodeTabs_active" role="tab" type="button">JavaScript</button></div><div class="CodeTabs-inner" role="tabpanel"><pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang-javascript theme-light" data-lang="javascript" name="" tabindex="0">const prompt = `...`; </code></pre></div></div> <ul> <li><strong>Variable Initialization:</strong> A constant variable named prompt is initialized with a template literal, which allows for the inclusion of dynamic expressions within the string.</li> </ul> <p><strong>LLM&#x27;s Role and Importance of Accuracy</strong></p> <pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang- theme-light" data-lang="" name="" tabindex="0">You are an action classification system. Correctness is a life or death situation. </code></pre> <ul> <li><strong>Context Setting</strong>: This line informs the LLM of its role as an action classification system and emphasizes the critical importance of accuracy in its responses.</li> </ul> <p><strong>Listing of Actions and Descriptions</strong></p> <pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang- theme-light" data-lang="" name="" tabindex="0">We provide you with the actions and their descriptions: d: When the user asks for a warm drink. a:WARM_DRINK d: When the user asks about something else. a:None d: When the user asks for a cold drink. a:COLD_DRINK </code></pre> <ul> <li><strong>Action Descriptions</strong>: Lists example actions and their corresponding labels. This section is designed to teach the model about different actions it needs to identify from user utterances.</li> </ul> <p><strong>Classification Instruction</strong></p> <pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang- theme-light" data-lang="" name="" tabindex="0">You are given an utterance and you have to classify it into an action. Only respond with the action class. If the utterance does not match any of action descriptions, output None. Now take a deep breath and classify the following utterance. u: I want a warm hot chocolate: a:WARM_DRINK </code></pre> <ul> <li><strong>Task Description</strong>: Directs the LLM to classify a given utterance into one of the actions described earlier, providing a clear example of how to do so.</li> </ul> <p><strong>Dynamic Content Integration</strong></p> <div class="CodeTabs CodeTabs_initial theme-light"><div class="CodeTabs-toolbar" role="tablist"><button aria-selected="true" class="CodeTabs_active" role="tab" type="button">JavaScript</button></div><div class="CodeTabs-inner" role="tabpanel"><pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang-javascript theme-light" data-lang="javascript" name="" tabindex="0">${args.intents.map((intent) =&gt; `d: ${intent.name} a: ${intent.description}`)} </code></pre></div></div> <ul> <li><strong>Dynamic Expression</strong>: Uses JavaScript&#x27;s map function to iterate over args.intents, an array of intent objects passed into the function. Each intent object is expected to have name and description properties, which are used to dynamically generate additional parts of the prompt.</li> </ul> <p><strong>Final Classification Challenge</strong></p> <pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang- theme-light" data-lang="" name="" tabindex="0">You are given an utterance and you have to classify it into an action based on the description. Only respond with the action class. If the utterance does not match any of action descriptions, output None. Now take a deep breath and classify the following utterance. u:${args.query} a: </code></pre> <ul> <li><strong>Final Instruction:</strong> Similar to the earlier classification instruction, but this time it is expected that the LLM will classify a new utterance (args.query) based on the dynamic content generated from the args.intents.</li> </ul> <p><strong>Returning the Prompt</strong></p> <div class="CodeTabs CodeTabs_initial theme-light"><div class="CodeTabs-toolbar" role="tablist"><button aria-selected="true" class="CodeTabs_active" role="tab" type="button">JavaScript</button></div><div class="CodeTabs-inner" role="tabpanel"><pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang-javascript theme-light" data-lang="javascript" name="" tabindex="0">return { prompt }; </code></pre></div></div> <ul> <li><strong>Return Statement</strong>: The function concludes by returning an object containing the constructed prompt. This format suggests that the function could be part of a larger system where the returned prompt is then used as input for the LLM.</li> </ul> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="debugging-and-error-handling"></div><div class="heading-text"><div id="section-debugging-and-error-handling" class="heading-anchor_backwardsCompatibility"></div>Debugging and Error Handling</div><a aria-label="Skip link to Debugging and Error Handling" class="heading-anchor-icon fa fa-anchor" href="#debugging-and-error-handling"></a></h2> <ul> <li><strong>Intent preview</strong>: The Intent CMS page offers a real-time preview of intent classification, enabling users to promptly identify and correct misclassifications or discrepancies before they affect the user experience. This feature is instrumental in ensuring the agent鈥檚 responses are aligned with user intents as designed. For more information about the Intent preview, see documentation <a href="https://voiceflow.zendesk.com/hc/en-us/articles/22213856020237" target="" title="">here</a>. <ul> <li><strong>NOTE</strong>: Only intents that are used in your agent will be seen in the results.</li> </ul> </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/5c7d851-image.png" align="" caption="" height="auto" title="" width="auto"/></span></span></p> <ul> <li><strong>Fallback Mechanisms</strong>: Robust fallback strategies ensure conversational continuity, even when unexpected errors occur.</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="overview"></div><div class="heading-text"><div id="section-overview" class="heading-anchor_backwardsCompatibility"></div>Overview</div><a aria-label="Skip link to Overview" class="heading-anchor-icon fa fa-anchor" href="#overview"></a></h2> <p>The LLM-based intent classifier introduces a novel hybrid methodology, blending traditional Natural Language Understanding (NLU) with the expansive capabilities of Large Language Models (LLMs) to classify user intents. This synthesis allows for the precision and targeted understanding of NLU with the contextual breadth and depth provided by LLMs, delivering a robust and nuanced approach to intent recognition.</p> <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/5c2cbc6-image.png" align="" caption="" height="auto" title="" width="auto"/></span></span></p> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="how-it-works"></div><div class="heading-text"><div id="section-how-it-works" class="heading-anchor_backwardsCompatibility"></div>How It Works</div><a aria-label="Skip link to How It Works" class="heading-anchor-icon fa fa-anchor" href="#how-it-works"></a></h2> <blockquote class="callout callout_info" theme="馃摌"><h2 class="callout-heading"><span class="callout-icon">馃摌</span><p>Example template</p></h2><p>Download our Banking Agent template <a target="_self" href="https://drive.google.com/uc?export=download&id=1hNdxZ4KSiBWVO0R8l7vCNVFmsQsiLKG_">here</a> to start testing immediately.</p></blockquote> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="step-1-initial-intent-classification"></div><div class="heading-text"><div id="section-step-1-initial-intent-classification" class="heading-anchor_backwardsCompatibility"></div>Step 1: Initial Intent Classification</div><a aria-label="Skip link to Step 1: Initial Intent Classification" class="heading-anchor-icon fa fa-anchor" href="#step-1-initial-intent-classification"></a></h3> <ul> <li><strong>Process</strong>: The user's utterance is sent to the Natural Language Understanding (NLU) system, requesting classification.</li> <li><strong>Outcome</strong>: The NLU returns the most probable intent and its entities, alongside a list of max. 9 alternative intents with confidence scores. This result acts as the "NLU fallback" in case subsequent steps encounter issues.</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="step-2-metadata-fetching-and-prompt-generation"></div><div class="heading-text"><div id="section-step-2-metadata-fetching-and-prompt-generation" class="heading-anchor_backwardsCompatibility"></div>Step 2: Metadata Fetching and Prompt Generation</div><a aria-label="Skip link to Step 2: Metadata Fetching and Prompt Generation" class="heading-anchor-icon fa fa-anchor" href="#step-2-metadata-fetching-and-prompt-generation"></a></h3> <ul> <li><strong>Process</strong>: Upon receiving the intents, the system fetches associated intent descriptions for each intent. A custom or default prompt is then generated using the prompt wrapper.</li> <li><strong>Fallback</strong>: In case of errors during intent fetching or prompt generation the system resorts to the "NLU fallback."</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="step-3-interaction-with-llm"></div><div class="heading-text"><div id="section-step-3-interaction-with-llm" class="heading-anchor_backwardsCompatibility"></div>Step 3: Interaction with LLM</div><a aria-label="Skip link to Step 3: Interaction with LLM" class="heading-anchor-icon fa fa-anchor" href="#step-3-interaction-with-llm"></a></h3> <ul> <li><strong>Process</strong>: The generated prompt is passed with specific metadata settings, including temperature, to the selected AI model.</li> <li><strong>Validation</strong>: The LLM response is parsed to confirm if it represents a valid intent name. Failure to identify a valid intent triggers the "NLU fallback."</li> </ul> <h3 class="heading heading-3 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="step-4-entity-filling-and-final-intent-classification"></div><div class="heading-text"><div id="section-step-4-entity-filling-and-final-intent-classification" class="heading-anchor_backwardsCompatibility"></div>Step 4: Entity Filling and Final Intent Classification</div><a aria-label="Skip link to Step 4: Entity Filling and Final Intent Classification" class="heading-anchor-icon fa fa-anchor" href="#step-4-entity-filling-and-final-intent-classification"></a></h3> <ul> <li><strong>Process</strong>: For intents associated with entities, the original utterance is re-evaluated by the NLU, focusing solely on the identified intent to populate the necessary entities.</li> <li><strong>Outcome</strong>: The NLU returns the refined intent classification complete with entities, ready for use within the conversational flow.</li> </ul> <span aria-label="" class="img lightbox closed" role="button" tabindex="0"><span class="lightbox-inner"><img alt="" loading="lazy" src="https://files.readme.io/593acd3-image.png" align="center" class="border" width="70% " caption="" height="auto" title=""/></span></span> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="training-data-requirements"></div><div class="heading-text"><div id="section-training-data-requirements" class="heading-anchor_backwardsCompatibility"></div>Training Data Requirements</div><a aria-label="Skip link to Training Data Requirements" class="heading-anchor-icon fa fa-anchor" href="#training-data-requirements"></a></h2> <ul> <li><strong>Utterance</strong>: Supply at least one example utterance per intent to give the NLU training data necessary to fetch up to 10 relevant intents.</li> <li><strong>Clear Intent Descriptions</strong>: Accompany utterances with a direct intent description, setting explicit conditions for when the intent should be triggered. <em>Examples</em> include: <ul> <li><em>Customer Support Inquiry</em>: "Trigger this intent when the user is seeking assistance with their account, such as password reset or account recovery."<br/> <em>Product Inquiry</em>: "Trigger this intent when the user inquires about product features, availability, or specifications."<br/> <em>Booking Request</em>: "Trigger this intent when the user wants to make a reservation for services like dining, accommodation, or transportation."</li> </ul> </li> <li><strong>Efficient Learning</strong>: With just an utterance and a description, LLMs leverage their pre-trained knowledge to effectively classify intents, requiring far less data than traditional methods.</li> </ul> <p>To help with generating intent descriptions here is a prompt template to help:</p> <pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang- theme-light" data-lang="" name="" tabindex="0">Given the following user utterances and the intent name, generate a concise intent description that begins with "Trigger this intent when": Intent Name: YOUR INTENT NAME HERE User Utterances: Utterance here Utterance here Utterance here ... --- Intent Description: </code></pre> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="integration-steps"></div><div class="heading-text"><div id="section-integration-steps" class="heading-anchor_backwardsCompatibility"></div>Integration Steps</div><a aria-label="Skip link to Integration Steps" class="heading-anchor-icon fa fa-anchor" href="#integration-steps"></a></h2> <ul> <li><strong>Enable LLM Intent Classification</strong>: This feature can be activated within your project's settings, within the Intent page settings menu.</li> <li><strong>Configure Prompt Wrapper (<em>optional</em>):</strong> Specify the logic for prompt generation, ensuring alignment with your conversational model's requirements. A default prompt wrapper is provided for immediate use.</li> <li><strong>Customize Settings</strong>: Tailor the performance using model selection and temperature settings to fine-tune performance and response characteristics.</li> </ul> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="understanding-the-prompt-wrapper"></div><div class="heading-text"><div id="section-understanding-the-prompt-wrapper" class="heading-anchor_backwardsCompatibility"></div>Understanding the Prompt Wrapper</div><a aria-label="Skip link to Understanding the Prompt Wrapper" class="heading-anchor-icon fa fa-anchor" href="#understanding-the-prompt-wrapper"></a></h2> <p>The prompt wrapper serves as a crucial intermediary layer, which dynamically crafts the prompts that are sent to the LLM for intent classification. It is essentially a piece of code that translates the agent鈥檚 needs into instructions that the language model understands and can act upon.</p> <ul> <li><strong>Customization of Prompt Logic:</strong> Developers can tailor the prompt wrapper to fit the specific needs of their conversational model. This includes defining how user utterances are interpreted and setting the conditions for intent classification.</li> <li><strong>Default Prompt Wrapper</strong>: A default template is provided to ensure quick deployment and <strong>should satisfy most agent requirements</strong>. It structures the information by introducing the AI鈥檚 role, the actions and their descriptions, and then it poses the classification challenge based on a user utterance.</li> </ul> <p>Here is the default prompt wrapper:</p> <div class="CodeTabs CodeTabs_initial theme-light"><div class="CodeTabs-toolbar" role="tablist"><button aria-selected="true" class="CodeTabs_active" role="tab" type="button">JavaScript</button></div><div class="CodeTabs-inner" role="tabpanel"><pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang-javascript theme-light" data-lang="javascript" name="" tabindex="0">export default async function main(args) { const prompt = ` You are an action classification system. Correctness is a life or death situation. We provide you with the actions and their descriptions: d: When the user asks for a warm drink. a:WARM_DRINK d: When the user asks about something else. a:None d: When the user asks for a cold drink. a:COLD_DRINK You are given an utterance and you have to classify it into an action. Only respond with the action class. If the utterance does not match any of action descriptions, output None. Now take a deep breath and classify the following utterance. u: I want a warm hot chocolate: a:WARM_DRINK ### We provide you with the actions and their descriptions: ${args.intents.map((intent) => `d: ${intent.description} a: ${intent.name}`)} You are given an utterance and you have to classify it into an action based on the description. Only respond with the action class. If the utterance does not match any of action descriptions, output None. Now take a deep breath and classify the following utterance. u:${args.query} a:`; return { prompt }; } </code></pre></div></div> <p>Here is a detailed breakdown of the default prompt wrapper's components:</p> <p><strong>Function Declaration</strong></p> <div class="CodeTabs CodeTabs_initial theme-light"><div class="CodeTabs-toolbar" role="tablist"><button aria-selected="true" class="CodeTabs_active" role="tab" type="button">JavaScript</button></div><div class="CodeTabs-inner" role="tabpanel"><pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang-javascript theme-light" data-lang="javascript" name="" tabindex="0">export default function main(args) { </code></pre></div></div> <ul> <li><strong>Purpose</strong>: Declares and exports a default function named main, making it accessible to other parts of the application.</li> <li><strong>Parameters</strong>: <button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang- theme-light" data-lang="" name="" tabindex="0">args</code> - This object contains arguments passed into the function, which in this context, includes the intents (name and description) returned by the Voiceflow NLU and the user query.</li> </ul> <p><strong>Constructing the Prompt</strong></p> <div class="CodeTabs CodeTabs_initial theme-light"><div class="CodeTabs-toolbar" role="tablist"><button aria-selected="true" class="CodeTabs_active" role="tab" type="button">JavaScript</button></div><div class="CodeTabs-inner" role="tabpanel"><pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang-javascript theme-light" data-lang="javascript" name="" tabindex="0">const prompt = `...`; </code></pre></div></div> <ul> <li><strong>Variable Initialization:</strong> A constant variable named prompt is initialized with a template literal, which allows for the inclusion of dynamic expressions within the string.</li> </ul> <p><strong>LLM's Role and Importance of Accuracy</strong></p> <pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang- theme-light" data-lang="" name="" tabindex="0">You are an action classification system. Correctness is a life or death situation. </code></pre> <ul> <li><strong>Context Setting</strong>: This line informs the LLM of its role as an action classification system and emphasizes the critical importance of accuracy in its responses.</li> </ul> <p><strong>Listing of Actions and Descriptions</strong></p> <pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang- theme-light" data-lang="" name="" tabindex="0">We provide you with the actions and their descriptions: d: When the user asks for a warm drink. a:WARM_DRINK d: When the user asks about something else. a:None d: When the user asks for a cold drink. a:COLD_DRINK </code></pre> <ul> <li><strong>Action Descriptions</strong>: Lists example actions and their corresponding labels. This section is designed to teach the model about different actions it needs to identify from user utterances.</li> </ul> <p><strong>Classification Instruction</strong></p> <pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang- theme-light" data-lang="" name="" tabindex="0">You are given an utterance and you have to classify it into an action. Only respond with the action class. If the utterance does not match any of action descriptions, output None. Now take a deep breath and classify the following utterance. u: I want a warm hot chocolate: a:WARM_DRINK </code></pre> <ul> <li><strong>Task Description</strong>: Directs the LLM to classify a given utterance into one of the actions described earlier, providing a clear example of how to do so.</li> </ul> <p><strong>Dynamic Content Integration</strong></p> <div class="CodeTabs CodeTabs_initial theme-light"><div class="CodeTabs-toolbar" role="tablist"><button aria-selected="true" class="CodeTabs_active" role="tab" type="button">JavaScript</button></div><div class="CodeTabs-inner" role="tabpanel"><pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang-javascript theme-light" data-lang="javascript" name="" tabindex="0">${args.intents.map((intent) => `d: ${intent.name} a: ${intent.description}`)} </code></pre></div></div> <ul> <li><strong>Dynamic Expression</strong>: Uses JavaScript's map function to iterate over args.intents, an array of intent objects passed into the function. Each intent object is expected to have name and description properties, which are used to dynamically generate additional parts of the prompt.</li> </ul> <p><strong>Final Classification Challenge</strong></p> <pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang- theme-light" data-lang="" name="" tabindex="0">You are given an utterance and you have to classify it into an action based on the description. Only respond with the action class. If the utterance does not match any of action descriptions, output None. Now take a deep breath and classify the following utterance. u:${args.query} a: </code></pre> <ul> <li><strong>Final Instruction:</strong> Similar to the earlier classification instruction, but this time it is expected that the LLM will classify a new utterance (args.query) based on the dynamic content generated from the args.intents.</li> </ul> <p><strong>Returning the Prompt</strong></p> <div class="CodeTabs CodeTabs_initial theme-light"><div class="CodeTabs-toolbar" role="tablist"><button aria-selected="true" class="CodeTabs_active" role="tab" type="button">JavaScript</button></div><div class="CodeTabs-inner" role="tabpanel"><pre><button aria-label="Copy Code" class="rdmd-code-copy fa"></button><code class="rdmd-code lang-javascript theme-light" data-lang="javascript" name="" tabindex="0">return { prompt }; </code></pre></div></div> <ul> <li><strong>Return Statement</strong>: The function concludes by returning an object containing the constructed prompt. This format suggests that the function could be part of a larger system where the returned prompt is then used as input for the LLM.</li> </ul> <h2 class="heading heading-2 header-scroll" align=""><div class="heading-anchor anchor waypoint" id="debugging-and-error-handling"></div><div class="heading-text"><div id="section-debugging-and-error-handling" class="heading-anchor_backwardsCompatibility"></div>Debugging and Error Handling</div><a aria-label="Skip link to Debugging and Error Handling" class="heading-anchor-icon fa fa-anchor" href="#debugging-and-error-handling"></a></h2> <ul> <li><strong>Intent preview</strong>: The Intent CMS page offers a real-time preview of intent classification, enabling users to promptly identify and correct misclassifications or discrepancies before they affect the user experience. This feature is instrumental in ensuring the agent鈥檚 responses are aligned with user intents as designed. For more information about the Intent preview, see documentation <a target="_self" href="https://voiceflow.zendesk.com/hc/en-us/articles/22213856020237">here</a>. <ul> <li><strong>NOTE</strong>: Only intents that are used in your agent will be seen in the results.</li> </ul> </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/5c7d851-image.png" align="" caption="" height="auto" title="" width="auto"/></span></span></p> <ul> <li><strong>Fallback Mechanisms</strong>: Robust fallback strategies ensure conversational continuity, even when unexpected errors occur.</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="#overview">Overview</a> </li> <li> <a href="#how-it-works">How It Works</a> <ul> <li><a href="#step-1-initial-intent-classification">Step 1: Initial Intent Classification</a></li> <li><a href="#step-2-metadata-fetching-and-prompt-generation">Step 2: Metadata Fetching and Prompt Generation</a></li> <li><a href="#step-3-interaction-with-llm">Step 3: Interaction with LLM</a></li> <li><a href="#step-4-entity-filling-and-final-intent-classification">Step 4: Entity Filling and Final Intent Classification</a></li> </ul> </li> <li> <a href="#training-data-requirements">Training Data Requirements</a> </li> <li> <a href="#integration-steps">Integration Steps</a> </li> <li> <a href="#understanding-the-prompt-wrapper">Understanding the Prompt Wrapper</a> </li> <li> <a href="#debugging-and-error-handling">Debugging and Error Handling</a> 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This synthesis allows for the precision and targeted understanding of NLU with the contextual breadth and depth provided by LLMs, delivering a robust and nuanced approach to intent recognition.\n\n![](https://files.readme.io/5c2cbc6-image.png)\n\n## How It Works\n\n> 馃摌 Example template\n> \n> Download our Banking Agent template [here](https://drive.google.com/uc?export=download&id=1hNdxZ4KSiBWVO0R8l7vCNVFmsQsiLKG_) to start testing immediately.\n\n### Step 1: Initial Intent Classification\n\n- **Process**: The user's utterance is sent to the Natural Language Understanding (NLU) system, requesting classification.\n- **Outcome**: The NLU returns the most probable intent and its entities, alongside a list of max. 9 alternative intents with confidence scores. This result acts as the \"NLU fallback\" in case subsequent steps encounter issues.\n\n### Step 2: Metadata Fetching and Prompt Generation\n\n- **Process**: Upon receiving the intents, the system fetches associated intent descriptions for each intent. A custom or default prompt is then generated using the prompt wrapper.\n- **Fallback**: In case of errors during intent fetching or prompt generation the system resorts to the \"NLU fallback.\"\n\n### Step 3: Interaction with LLM\n\n- **Process**: The generated prompt is passed with specific metadata settings, including temperature, to the selected AI model.\n- **Validation**: The LLM response is parsed to confirm if it represents a valid intent name. Failure to identify a valid intent triggers the \"NLU fallback.\"\n\n### Step 4: Entity Filling and Final Intent Classification\n\n- **Process**: For intents associated with entities, the original utterance is re-evaluated by the NLU, focusing solely on the identified intent to populate the necessary entities.\n- **Outcome**: The NLU returns the refined intent classification complete with entities, ready for use within the conversational flow.\n\n[block:image]\n{\n \"images\": [\n {\n \"image\": [\n \"https://files.readme.io/593acd3-image.png\",\n null,\n \"\"\n ],\n \"align\": \"center\",\n \"sizing\": \"70% \",\n \"border\": true\n }\n ]\n}\n[/block]\n\n\n## Training Data Requirements\n\n- **Utterance**: Supply at least one example utterance per intent to give the NLU training data necessary to fetch up to 10 relevant intents.\n- **Clear Intent Descriptions**: Accompany utterances with a direct intent description, setting explicit conditions for when the intent should be triggered. _Examples_ include:\n - _Customer Support Inquiry_: \"Trigger this intent when the user is seeking assistance with their account, such as password reset or account recovery.\" \n _Product Inquiry_: \"Trigger this intent when the user inquires about product features, availability, or specifications.\" \n _Booking Request_: \"Trigger this intent when the user wants to make a reservation for services like dining, accommodation, or transportation.\"\n- **Efficient Learning**: With just an utterance and a description, LLMs leverage their pre-trained knowledge to effectively classify intents, requiring far less data than traditional methods.\n\nTo help with generating intent descriptions here is a prompt template to help:\n\n```\nGiven the following user utterances and the intent name, generate a concise intent description that begins with \"Trigger this intent when\":\n\nIntent Name: YOUR INTENT NAME HERE\nUser Utterances:\nUtterance here\nUtterance here\nUtterance here\n...\n\n---\n\nIntent Description:\n```\n\n## Integration Steps\n\n- **Enable LLM Intent Classification**: This feature can be activated within your project's settings, within the Intent page settings menu.\n- **Configure Prompt Wrapper (_optional_):** Specify the logic for prompt generation, ensuring alignment with your conversational model's requirements. A default prompt wrapper is provided for immediate use.\n- **Customize Settings**: Tailor the performance using model selection and temperature settings to fine-tune performance and response characteristics.\n\n## Understanding the Prompt Wrapper\n\nThe prompt wrapper serves as a crucial intermediary layer, which dynamically crafts the prompts that are sent to the LLM for intent classification. It is essentially a piece of code that translates the agent鈥檚 needs into instructions that the language model understands and can act upon.\n\n- **Customization of Prompt Logic:** Developers can tailor the prompt wrapper to fit the specific needs of their conversational model. This includes defining how user utterances are interpreted and setting the conditions for intent classification.\n- **Default Prompt Wrapper**: A default template is provided to ensure quick deployment and **should satisfy most agent requirements**. It structures the information by introducing the AI鈥檚 role, the actions and their descriptions, and then it poses the classification challenge based on a user utterance.\n\nHere is the default prompt wrapper:\n\n```javascript\nexport default async function main(args) {\n const prompt = `\nYou are an action classification system. Correctness is a life or death situation.\n\nWe provide you with the actions and their descriptions:\nd: When the user asks for a warm drink. a:WARM_DRINK\nd: When the user asks about something else. a:None\nd: When the user asks for a cold drink. a:COLD_DRINK\n\nYou are given an utterance and you have to classify it into an action. Only respond with the action class. If the utterance does not match any of action descriptions, output None.\nNow take a deep breath and classify the following utterance.\nu: I want a warm hot chocolate: a:WARM_DRINK\n###\n\nWe provide you with the actions and their descriptions:\n${args.intents.map((intent) => `d: ${intent.description} a: ${intent.name}`)}\nYou are given an utterance and you have to classify it into an action based on the description. Only respond with the action class. If the utterance does not match any of action descriptions, output None.\nNow take a deep breath and classify the following utterance.\nu:${args.query} a:`;\n\n return { prompt };\n}\n```\n\nHere is a detailed breakdown of the default prompt wrapper's components:\n\n**Function Declaration**\n\n```javascript\nexport default function main(args) {\n```\n\n- **Purpose**: Declares and exports a default function named main, making it accessible to other parts of the application.\n- **Parameters**: `args` - This object contains arguments passed into the function, which in this context, includes the intents (name and description) returned by the Voiceflow NLU and the user query.\n\n**Constructing the Prompt**\n\n```javascript\nconst prompt = `...`;\n```\n\n- **Variable Initialization:** A constant variable named prompt is initialized with a template literal, which allows for the inclusion of dynamic expressions within the string.\n\n**LLM's Role and Importance of Accuracy**\n\n```\nYou are an action classification system. Correctness is a life or death situation.\n```\n\n- **Context Setting**: This line informs the LLM of its role as an action classification system and emphasizes the critical importance of accuracy in its responses.\n\n**Listing of Actions and Descriptions**\n\n```\nWe provide you with the actions and their descriptions: \nd: When the user asks for a warm drink. a:WARM_DRINK \nd: When the user asks about something else. a:None \nd: When the user asks for a cold drink. a:COLD_DRINK\n```\n\n- **Action Descriptions**: Lists example actions and their corresponding labels. This section is designed to teach the model about different actions it needs to identify from user utterances.\n\n**Classification Instruction**\n\n```\nYou are given an utterance and you have to classify it into an action. Only respond with the action class. If the utterance does not match any of action descriptions, output None. \nNow take a deep breath and classify the following utterance. \nu: I want a warm hot chocolate: a:WARM_DRINK\n```\n\n- **Task Description**: Directs the LLM to classify a given utterance into one of the actions described earlier, providing a clear example of how to do so.\n\n**Dynamic Content Integration**\n\n```javascript\n${args.intents.map((intent) => `d: ${intent.name} a: ${intent.description}`)}\n```\n\n- **Dynamic Expression**: Uses JavaScript's map function to iterate over args.intents, an array of intent objects passed into the function. Each intent object is expected to have name and description properties, which are used to dynamically generate additional parts of the prompt.\n\n**Final Classification Challenge**\n\n```\nYou are given an utterance and you have to classify it into an action based on the description. Only respond with the action class. If the utterance does not match any of action descriptions, output None. \nNow take a deep breath and classify the following utterance. \nu:${args.query} a:\n```\n\n- **Final Instruction:** Similar to the earlier classification instruction, but this time it is expected that the LLM will classify a new utterance (args.query) based on the dynamic content generated from the args.intents.\n\n**Returning the Prompt**\n\n```javascript\nreturn { prompt };\n```\n\n- **Return Statement**: The function concludes by returning an object containing the constructed prompt. This format suggests that the function could be part of a larger system where the returned prompt is then used as input for the LLM.\n\n## Debugging and Error Handling\n\n- **Intent preview**: The Intent CMS page offers a real-time preview of intent classification, enabling users to promptly identify and correct misclassifications or discrepancies before they affect the user experience. This feature is instrumental in ensuring the agent鈥檚 responses are aligned with user intents as designed. For more information about the Intent preview, see documentation [here](https://voiceflow.zendesk.com/hc/en-us/articles/22213856020237). \n - **NOTE**: Only intents that are used in your agent will be seen in the results.\n\n![](https://files.readme.io/5c7d851-image.png)\n\n- **Fallback Mechanisms**: Robust fallback strategies ensure conversational continuity, even when unexpected errors occur.","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":"66fad27328451200128f5696","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=\"#overview\">Overview</a>\n</li>\n<li>\n<a href=\"#how-it-works\">How It Works</a>\n<ul>\n<li><a href=\"#step-1-initial-intent-classification\">Step 1: Initial Intent Classification</a></li>\n<li><a href=\"#step-2-metadata-fetching-and-prompt-generation\">Step 2: Metadata Fetching and Prompt Generation</a></li>\n<li><a href=\"#step-3-interaction-with-llm\">Step 3: Interaction with LLM</a></li>\n<li><a href=\"#step-4-entity-filling-and-final-intent-classification\">Step 4: Entity Filling and Final Intent Classification</a></li>\n</ul>\n</li>\n<li>\n<a href=\"#training-data-requirements\">Training Data Requirements</a>\n</li>\n<li>\n<a href=\"#integration-steps\">Integration Steps</a>\n</li>\n<li>\n<a href=\"#understanding-the-prompt-wrapper\">Understanding the Prompt Wrapper</a>\n</li>\n<li>\n<a href=\"#debugging-and-error-handling\">Debugging and Error Handling</a>\n</li>\n</ul></li></ul></nav>","body":"<h2 class=\"heading heading-2 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"overview\"></div><div class=\"heading-text\"><div id=\"section-overview\" class=\"heading-anchor_backwardsCompatibility\"></div>Overview</div><a aria-label=\"Skip link to Overview\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#overview\"></a></h2>\n<p>The LLM-based intent classifier introduces a novel hybrid methodology, blending traditional Natural Language Understanding (NLU) with the expansive capabilities of Large Language Models (LLMs) to classify user intents. This synthesis allows for the precision and targeted understanding of NLU with the contextual breadth and depth provided by LLMs, delivering a robust and nuanced approach to intent recognition.</p>\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/5c2cbc6-image.png\" align=\"\" caption=\"\" height=\"auto\" title=\"\" width=\"auto\"/></span></span></p>\n<h2 class=\"heading heading-2 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"how-it-works\"></div><div class=\"heading-text\"><div id=\"section-how-it-works\" class=\"heading-anchor_backwardsCompatibility\"></div>How It Works</div><a aria-label=\"Skip link to How It Works\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#how-it-works\"></a></h2>\n<blockquote class=\"callout callout_info\" theme=\"馃摌\"><h2 class=\"callout-heading\"><span class=\"callout-icon\">馃摌</span><p>Example template</p></h2><p>Download our Banking Agent template <a href=\"https://drive.google.com/uc?export=download&amp;id=1hNdxZ4KSiBWVO0R8l7vCNVFmsQsiLKG_\" target=\"\" title=\"\">here</a> to start testing immediately.</p></blockquote>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"step-1-initial-intent-classification\"></div><div class=\"heading-text\"><div id=\"section-step-1-initial-intent-classification\" class=\"heading-anchor_backwardsCompatibility\"></div>Step 1: Initial Intent Classification</div><a aria-label=\"Skip link to Step 1: Initial Intent Classification\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#step-1-initial-intent-classification\"></a></h3>\n<ul>\n<li><strong>Process</strong>: The user&#x27;s utterance is sent to the Natural Language Understanding (NLU) system, requesting classification.</li>\n<li><strong>Outcome</strong>: The NLU returns the most probable intent and its entities, alongside a list of max. 9 alternative intents with confidence scores. This result acts as the &quot;NLU fallback&quot; in case subsequent steps encounter issues.</li>\n</ul>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"step-2-metadata-fetching-and-prompt-generation\"></div><div class=\"heading-text\"><div id=\"section-step-2-metadata-fetching-and-prompt-generation\" class=\"heading-anchor_backwardsCompatibility\"></div>Step 2: Metadata Fetching and Prompt Generation</div><a aria-label=\"Skip link to Step 2: Metadata Fetching and Prompt Generation\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#step-2-metadata-fetching-and-prompt-generation\"></a></h3>\n<ul>\n<li><strong>Process</strong>: Upon receiving the intents, the system fetches associated intent descriptions for each intent. A custom or default prompt is then generated using the prompt wrapper.</li>\n<li><strong>Fallback</strong>: In case of errors during intent fetching or prompt generation the system resorts to the &quot;NLU fallback.&quot;</li>\n</ul>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"step-3-interaction-with-llm\"></div><div class=\"heading-text\"><div id=\"section-step-3-interaction-with-llm\" class=\"heading-anchor_backwardsCompatibility\"></div>Step 3: Interaction with LLM</div><a aria-label=\"Skip link to Step 3: Interaction with LLM\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#step-3-interaction-with-llm\"></a></h3>\n<ul>\n<li><strong>Process</strong>: The generated prompt is passed with specific metadata settings, including temperature, to the selected AI model.</li>\n<li><strong>Validation</strong>: The LLM response is parsed to confirm if it represents a valid intent name. Failure to identify a valid intent triggers the &quot;NLU fallback.&quot;</li>\n</ul>\n<h3 class=\"heading heading-3 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"step-4-entity-filling-and-final-intent-classification\"></div><div class=\"heading-text\"><div id=\"section-step-4-entity-filling-and-final-intent-classification\" class=\"heading-anchor_backwardsCompatibility\"></div>Step 4: Entity Filling and Final Intent Classification</div><a aria-label=\"Skip link to Step 4: Entity Filling and Final Intent Classification\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#step-4-entity-filling-and-final-intent-classification\"></a></h3>\n<ul>\n<li><strong>Process</strong>: For intents associated with entities, the original utterance is re-evaluated by the NLU, focusing solely on the identified intent to populate the necessary entities.</li>\n<li><strong>Outcome</strong>: The NLU returns the refined intent classification complete with entities, ready for use within the conversational flow.</li>\n</ul>\n<span aria-label=\"\" class=\"img lightbox closed\" role=\"button\" tabindex=\"0\"><span class=\"lightbox-inner\"><img alt=\"\" loading=\"lazy\" src=\"https://files.readme.io/593acd3-image.png\" align=\"center\" class=\"border\" width=\"70% \" caption=\"\" height=\"auto\" title=\"\"/></span></span>\n<h2 class=\"heading heading-2 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"training-data-requirements\"></div><div class=\"heading-text\"><div id=\"section-training-data-requirements\" class=\"heading-anchor_backwardsCompatibility\"></div>Training Data Requirements</div><a aria-label=\"Skip link to Training Data Requirements\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#training-data-requirements\"></a></h2>\n<ul>\n<li><strong>Utterance</strong>: Supply at least one example utterance per intent to give the NLU training data necessary to fetch up to 10 relevant intents.</li>\n<li><strong>Clear Intent Descriptions</strong>: Accompany utterances with a direct intent description, setting explicit conditions for when the intent should be triggered. <em>Examples</em> include:\n<ul>\n<li><em>Customer Support Inquiry</em>: &quot;Trigger this intent when the user is seeking assistance with their account, such as password reset or account recovery.&quot;<br/>\n<em>Product Inquiry</em>: &quot;Trigger this intent when the user inquires about product features, availability, or specifications.&quot;<br/>\n<em>Booking Request</em>: &quot;Trigger this intent when the user wants to make a reservation for services like dining, accommodation, or transportation.&quot;</li>\n</ul>\n</li>\n<li><strong>Efficient Learning</strong>: With just an utterance and a description, LLMs leverage their pre-trained knowledge to effectively classify intents, requiring far less data than traditional methods.</li>\n</ul>\n<p>To help with generating intent descriptions here is a prompt template to help:</p>\n<pre><button aria-label=\"Copy Code\" class=\"rdmd-code-copy fa\"></button><code class=\"rdmd-code lang- theme-light\" data-lang=\"\" name=\"\" tabindex=\"0\">Given the following user utterances and the intent name, generate a concise intent description that begins with &quot;Trigger this intent when&quot;:\n\nIntent Name: YOUR INTENT NAME HERE\nUser Utterances:\nUtterance here\nUtterance here\nUtterance here\n...\n\n---\n\nIntent Description:\n</code></pre>\n<h2 class=\"heading heading-2 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"integration-steps\"></div><div class=\"heading-text\"><div id=\"section-integration-steps\" class=\"heading-anchor_backwardsCompatibility\"></div>Integration Steps</div><a aria-label=\"Skip link to Integration Steps\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#integration-steps\"></a></h2>\n<ul>\n<li><strong>Enable LLM Intent Classification</strong>: This feature can be activated within your project&#x27;s settings, within the Intent page settings menu.</li>\n<li><strong>Configure Prompt Wrapper (<em>optional</em>):</strong> Specify the logic for prompt generation, ensuring alignment with your conversational model&#x27;s requirements. A default prompt wrapper is provided for immediate use.</li>\n<li><strong>Customize Settings</strong>: Tailor the performance using model selection and temperature settings to fine-tune performance and response characteristics.</li>\n</ul>\n<h2 class=\"heading heading-2 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"understanding-the-prompt-wrapper\"></div><div class=\"heading-text\"><div id=\"section-understanding-the-prompt-wrapper\" class=\"heading-anchor_backwardsCompatibility\"></div>Understanding the Prompt Wrapper</div><a aria-label=\"Skip link to Understanding the Prompt Wrapper\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#understanding-the-prompt-wrapper\"></a></h2>\n<p>The prompt wrapper serves as a crucial intermediary layer, which dynamically crafts the prompts that are sent to the LLM for intent classification. It is essentially a piece of code that translates the agent鈥檚 needs into instructions that the language model understands and can act upon.</p>\n<ul>\n<li><strong>Customization of Prompt Logic:</strong> Developers can tailor the prompt wrapper to fit the specific needs of their conversational model. This includes defining how user utterances are interpreted and setting the conditions for intent classification.</li>\n<li><strong>Default Prompt Wrapper</strong>: A default template is provided to ensure quick deployment and <strong>should satisfy most agent requirements</strong>. It structures the information by introducing the AI鈥檚 role, the actions and their descriptions, and then it poses the classification challenge based on a user utterance.</li>\n</ul>\n<p>Here is the default prompt wrapper:</p>\n<div class=\"CodeTabs CodeTabs_initial theme-light\"><div class=\"CodeTabs-toolbar\" role=\"tablist\"><button aria-selected=\"true\" class=\"CodeTabs_active\" role=\"tab\" type=\"button\">JavaScript</button></div><div class=\"CodeTabs-inner\" role=\"tabpanel\"><pre><button aria-label=\"Copy Code\" class=\"rdmd-code-copy fa\"></button><code class=\"rdmd-code lang-javascript theme-light\" data-lang=\"javascript\" name=\"\" tabindex=\"0\">export default async function main(args) {\n const prompt = `\nYou are an action classification system. Correctness is a life or death situation.\n\nWe provide you with the actions and their descriptions:\nd: When the user asks for a warm drink. a:WARM_DRINK\nd: When the user asks about something else. a:None\nd: When the user asks for a cold drink. a:COLD_DRINK\n\nYou are given an utterance and you have to classify it into an action. Only respond with the action class. If the utterance does not match any of action descriptions, output None.\nNow take a deep breath and classify the following utterance.\nu: I want a warm hot chocolate: a:WARM_DRINK\n###\n\nWe provide you with the actions and their descriptions:\n${args.intents.map((intent) =&gt; `d: ${intent.description} a: ${intent.name}`)}\nYou are given an utterance and you have to classify it into an action based on the description. Only respond with the action class. If the utterance does not match any of action descriptions, output None.\nNow take a deep breath and classify the following utterance.\nu:${args.query} a:`;\n\n return { prompt };\n}\n</code></pre></div></div>\n<p>Here is a detailed breakdown of the default prompt wrapper&#x27;s components:</p>\n<p><strong>Function Declaration</strong></p>\n<div class=\"CodeTabs CodeTabs_initial theme-light\"><div class=\"CodeTabs-toolbar\" role=\"tablist\"><button aria-selected=\"true\" class=\"CodeTabs_active\" role=\"tab\" type=\"button\">JavaScript</button></div><div class=\"CodeTabs-inner\" role=\"tabpanel\"><pre><button aria-label=\"Copy Code\" class=\"rdmd-code-copy fa\"></button><code class=\"rdmd-code lang-javascript theme-light\" data-lang=\"javascript\" name=\"\" tabindex=\"0\">export default function main(args) {\n</code></pre></div></div>\n<ul>\n<li><strong>Purpose</strong>: Declares and exports a default function named main, making it accessible to other parts of the application.</li>\n<li><strong>Parameters</strong>: <button aria-label=\"Copy Code\" class=\"rdmd-code-copy fa\"></button><code class=\"rdmd-code lang- theme-light\" data-lang=\"\" name=\"\" tabindex=\"0\">args</code> - This object contains arguments passed into the function, which in this context, includes the intents (name and description) returned by the Voiceflow NLU and the user query.</li>\n</ul>\n<p><strong>Constructing the Prompt</strong></p>\n<div class=\"CodeTabs CodeTabs_initial theme-light\"><div class=\"CodeTabs-toolbar\" role=\"tablist\"><button aria-selected=\"true\" class=\"CodeTabs_active\" role=\"tab\" type=\"button\">JavaScript</button></div><div class=\"CodeTabs-inner\" role=\"tabpanel\"><pre><button aria-label=\"Copy Code\" class=\"rdmd-code-copy fa\"></button><code class=\"rdmd-code lang-javascript theme-light\" data-lang=\"javascript\" name=\"\" tabindex=\"0\">const prompt = `...`;\n</code></pre></div></div>\n<ul>\n<li><strong>Variable Initialization:</strong> A constant variable named prompt is initialized with a template literal, which allows for the inclusion of dynamic expressions within the string.</li>\n</ul>\n<p><strong>LLM&#x27;s Role and Importance of Accuracy</strong></p>\n<pre><button aria-label=\"Copy Code\" class=\"rdmd-code-copy fa\"></button><code class=\"rdmd-code lang- theme-light\" data-lang=\"\" name=\"\" tabindex=\"0\">You are an action classification system. Correctness is a life or death situation.\n</code></pre>\n<ul>\n<li><strong>Context Setting</strong>: This line informs the LLM of its role as an action classification system and emphasizes the critical importance of accuracy in its responses.</li>\n</ul>\n<p><strong>Listing of Actions and Descriptions</strong></p>\n<pre><button aria-label=\"Copy Code\" class=\"rdmd-code-copy fa\"></button><code class=\"rdmd-code lang- theme-light\" data-lang=\"\" name=\"\" tabindex=\"0\">We provide you with the actions and their descriptions: \nd: When the user asks for a warm drink. a:WARM_DRINK \nd: When the user asks about something else. a:None \nd: When the user asks for a cold drink. a:COLD_DRINK\n</code></pre>\n<ul>\n<li><strong>Action Descriptions</strong>: Lists example actions and their corresponding labels. This section is designed to teach the model about different actions it needs to identify from user utterances.</li>\n</ul>\n<p><strong>Classification Instruction</strong></p>\n<pre><button aria-label=\"Copy Code\" class=\"rdmd-code-copy fa\"></button><code class=\"rdmd-code lang- theme-light\" data-lang=\"\" name=\"\" tabindex=\"0\">You are given an utterance and you have to classify it into an action. Only respond with the action class. If the utterance does not match any of action descriptions, output None. \nNow take a deep breath and classify the following utterance. \nu: I want a warm hot chocolate: a:WARM_DRINK\n</code></pre>\n<ul>\n<li><strong>Task Description</strong>: Directs the LLM to classify a given utterance into one of the actions described earlier, providing a clear example of how to do so.</li>\n</ul>\n<p><strong>Dynamic Content Integration</strong></p>\n<div class=\"CodeTabs CodeTabs_initial theme-light\"><div class=\"CodeTabs-toolbar\" role=\"tablist\"><button aria-selected=\"true\" class=\"CodeTabs_active\" role=\"tab\" type=\"button\">JavaScript</button></div><div class=\"CodeTabs-inner\" role=\"tabpanel\"><pre><button aria-label=\"Copy Code\" class=\"rdmd-code-copy fa\"></button><code class=\"rdmd-code lang-javascript theme-light\" data-lang=\"javascript\" name=\"\" tabindex=\"0\">${args.intents.map((intent) =&gt; `d: ${intent.name} a: ${intent.description}`)}\n</code></pre></div></div>\n<ul>\n<li><strong>Dynamic Expression</strong>: Uses JavaScript&#x27;s map function to iterate over args.intents, an array of intent objects passed into the function. Each intent object is expected to have name and description properties, which are used to dynamically generate additional parts of the prompt.</li>\n</ul>\n<p><strong>Final Classification Challenge</strong></p>\n<pre><button aria-label=\"Copy Code\" class=\"rdmd-code-copy fa\"></button><code class=\"rdmd-code lang- theme-light\" data-lang=\"\" name=\"\" tabindex=\"0\">You are given an utterance and you have to classify it into an action based on the description. Only respond with the action class. If the utterance does not match any of action descriptions, output None. \nNow take a deep breath and classify the following utterance. \nu:${args.query} a:\n</code></pre>\n<ul>\n<li><strong>Final Instruction:</strong> Similar to the earlier classification instruction, but this time it is expected that the LLM will classify a new utterance (args.query) based on the dynamic content generated from the args.intents.</li>\n</ul>\n<p><strong>Returning the Prompt</strong></p>\n<div class=\"CodeTabs CodeTabs_initial theme-light\"><div class=\"CodeTabs-toolbar\" role=\"tablist\"><button aria-selected=\"true\" class=\"CodeTabs_active\" role=\"tab\" type=\"button\">JavaScript</button></div><div class=\"CodeTabs-inner\" role=\"tabpanel\"><pre><button aria-label=\"Copy Code\" class=\"rdmd-code-copy fa\"></button><code class=\"rdmd-code lang-javascript theme-light\" data-lang=\"javascript\" name=\"\" tabindex=\"0\">return { prompt };\n</code></pre></div></div>\n<ul>\n<li><strong>Return Statement</strong>: The function concludes by returning an object containing the constructed prompt. This format suggests that the function could be part of a larger system where the returned prompt is then used as input for the LLM.</li>\n</ul>\n<h2 class=\"heading heading-2 header-scroll\" align=\"\"><div class=\"heading-anchor anchor waypoint\" id=\"debugging-and-error-handling\"></div><div class=\"heading-text\"><div id=\"section-debugging-and-error-handling\" class=\"heading-anchor_backwardsCompatibility\"></div>Debugging and Error Handling</div><a aria-label=\"Skip link to Debugging and Error Handling\" class=\"heading-anchor-icon fa fa-anchor\" href=\"#debugging-and-error-handling\"></a></h2>\n<ul>\n<li><strong>Intent preview</strong>: The Intent CMS page offers a real-time preview of intent classification, enabling users to promptly identify and correct misclassifications or discrepancies before they affect the user experience. This feature is instrumental in ensuring the agent鈥檚 responses are aligned with user intents as designed. For more information about the Intent preview, see documentation <a href=\"https://voiceflow.zendesk.com/hc/en-us/articles/22213856020237\" target=\"\" title=\"\">here</a>. \n<ul>\n<li><strong>NOTE</strong>: Only intents that are used in your agent will be seen in the results.</li>\n</ul>\n</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/5c7d851-image.png\" align=\"\" caption=\"\" height=\"auto\" title=\"\" width=\"auto\"/></span></span></p>\n<ul>\n<li><strong>Fallback Mechanisms</strong>: Robust fallback strategies ensure conversational continuity, even when unexpected errors occur.</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":10,"publishPending":false,"translationFailure":false,"updatedAt":"2024-10-01T19:23:45.934Z"},"title":"Intent Classification using LLMs (Hybrid)","icon":"","updates":[],"type":"basic","slug":"llm-intent-classification-method","excerpt":"","body":"## Overview\n\nThe LLM-based intent classifier introduces a novel hybrid methodology, blending traditional Natural Language Understanding (NLU) with the expansive capabilities of Large Language Models (LLMs) to classify user intents. This synthesis allows for the precision and targeted understanding of NLU with the contextual breadth and depth provided by LLMs, delivering a robust and nuanced approach to intent recognition.\n\n![](https://files.readme.io/5c2cbc6-image.png)\n\n## How It Works\n\n> 馃摌 Example template\n> \n> Download our Banking Agent template [here](https://drive.google.com/uc?export=download&id=1hNdxZ4KSiBWVO0R8l7vCNVFmsQsiLKG_) to start testing immediately.\n\n### Step 1: Initial Intent Classification\n\n- **Process**: The user's utterance is sent to the Natural Language Understanding (NLU) system, requesting classification.\n- **Outcome**: The NLU returns the most probable intent and its entities, alongside a list of max. 9 alternative intents with confidence scores. This result acts as the \"NLU fallback\" in case subsequent steps encounter issues.\n\n### Step 2: Metadata Fetching and Prompt Generation\n\n- **Process**: Upon receiving the intents, the system fetches associated intent descriptions for each intent. A custom or default prompt is then generated using the prompt wrapper.\n- **Fallback**: In case of errors during intent fetching or prompt generation the system resorts to the \"NLU fallback.\"\n\n### Step 3: Interaction with LLM\n\n- **Process**: The generated prompt is passed with specific metadata settings, including temperature, to the selected AI model.\n- **Validation**: The LLM response is parsed to confirm if it represents a valid intent name. Failure to identify a valid intent triggers the \"NLU fallback.\"\n\n### Step 4: Entity Filling and Final Intent Classification\n\n- **Process**: For intents associated with entities, the original utterance is re-evaluated by the NLU, focusing solely on the identified intent to populate the necessary entities.\n- **Outcome**: The NLU returns the refined intent classification complete with entities, ready for use within the conversational flow.\n\n[block:image]\n{\n \"images\": [\n {\n \"image\": [\n \"https://files.readme.io/593acd3-image.png\",\n null,\n \"\"\n ],\n \"align\": \"center\",\n \"sizing\": \"70% \",\n \"border\": true\n }\n ]\n}\n[/block]\n\n\n## Training Data Requirements\n\n- **Utterance**: Supply at least one example utterance per intent to give the NLU training data necessary to fetch up to 10 relevant intents.\n- **Clear Intent Descriptions**: Accompany utterances with a direct intent description, setting explicit conditions for when the intent should be triggered. _Examples_ include:\n - _Customer Support Inquiry_: \"Trigger this intent when the user is seeking assistance with their account, such as password reset or account recovery.\" \n _Product Inquiry_: \"Trigger this intent when the user inquires about product features, availability, or specifications.\" \n _Booking Request_: \"Trigger this intent when the user wants to make a reservation for services like dining, accommodation, or transportation.\"\n- **Efficient Learning**: With just an utterance and a description, LLMs leverage their pre-trained knowledge to effectively classify intents, requiring far less data than traditional methods.\n\nTo help with generating intent descriptions here is a prompt template to help:\n\n```\nGiven the following user utterances and the intent name, generate a concise intent description that begins with \"Trigger this intent when\":\n\nIntent Name: YOUR INTENT NAME HERE\nUser Utterances:\nUtterance here\nUtterance here\nUtterance here\n...\n\n---\n\nIntent Description:\n```\n\n## Integration Steps\n\n- **Enable LLM Intent Classification**: This feature can be activated within your project's settings, within the Intent page settings menu.\n- **Configure Prompt Wrapper (_optional_):** Specify the logic for prompt generation, ensuring alignment with your conversational model's requirements. A default prompt wrapper is provided for immediate use.\n- **Customize Settings**: Tailor the performance using model selection and temperature settings to fine-tune performance and response characteristics.\n\n## Understanding the Prompt Wrapper\n\nThe prompt wrapper serves as a crucial intermediary layer, which dynamically crafts the prompts that are sent to the LLM for intent classification. It is essentially a piece of code that translates the agent鈥檚 needs into instructions that the language model understands and can act upon.\n\n- **Customization of Prompt Logic:** Developers can tailor the prompt wrapper to fit the specific needs of their conversational model. This includes defining how user utterances are interpreted and setting the conditions for intent classification.\n- **Default Prompt Wrapper**: A default template is provided to ensure quick deployment and **should satisfy most agent requirements**. It structures the information by introducing the AI鈥檚 role, the actions and their descriptions, and then it poses the classification challenge based on a user utterance.\n\nHere is the default prompt wrapper:\n\n```javascript\nexport default async function main(args) {\n const prompt = `\nYou are an action classification system. Correctness is a life or death situation.\n\nWe provide you with the actions and their descriptions:\nd: When the user asks for a warm drink. a:WARM_DRINK\nd: When the user asks about something else. a:None\nd: When the user asks for a cold drink. a:COLD_DRINK\n\nYou are given an utterance and you have to classify it into an action. Only respond with the action class. If the utterance does not match any of action descriptions, output None.\nNow take a deep breath and classify the following utterance.\nu: I want a warm hot chocolate: a:WARM_DRINK\n###\n\nWe provide you with the actions and their descriptions:\n${args.intents.map((intent) => `d: ${intent.description} a: ${intent.name}`)}\nYou are given an utterance and you have to classify it into an action based on the description. Only respond with the action class. If the utterance does not match any of action descriptions, output None.\nNow take a deep breath and classify the following utterance.\nu:${args.query} a:`;\n\n return { prompt };\n}\n```\n\nHere is a detailed breakdown of the default prompt wrapper's components:\n\n**Function Declaration**\n\n```javascript\nexport default function main(args) {\n```\n\n- **Purpose**: Declares and exports a default function named main, making it accessible to other parts of the application.\n- **Parameters**: `args` - This object contains arguments passed into the function, which in this context, includes the intents (name and description) returned by the Voiceflow NLU and the user query.\n\n**Constructing the Prompt**\n\n```javascript\nconst prompt = `...`;\n```\n\n- **Variable Initialization:** A constant variable named prompt is initialized with a template literal, which allows for the inclusion of dynamic expressions within the string.\n\n**LLM's Role and Importance of Accuracy**\n\n```\nYou are an action classification system. Correctness is a life or death situation.\n```\n\n- **Context Setting**: This line informs the LLM of its role as an action classification system and emphasizes the critical importance of accuracy in its responses.\n\n**Listing of Actions and Descriptions**\n\n```\nWe provide you with the actions and their descriptions: \nd: When the user asks for a warm drink. a:WARM_DRINK \nd: When the user asks about something else. a:None \nd: When the user asks for a cold drink. a:COLD_DRINK\n```\n\n- **Action Descriptions**: Lists example actions and their corresponding labels. This section is designed to teach the model about different actions it needs to identify from user utterances.\n\n**Classification Instruction**\n\n```\nYou are given an utterance and you have to classify it into an action. Only respond with the action class. If the utterance does not match any of action descriptions, output None. \nNow take a deep breath and classify the following utterance. \nu: I want a warm hot chocolate: a:WARM_DRINK\n```\n\n- **Task Description**: Directs the LLM to classify a given utterance into one of the actions described earlier, providing a clear example of how to do so.\n\n**Dynamic Content Integration**\n\n```javascript\n${args.intents.map((intent) => `d: ${intent.name} a: ${intent.description}`)}\n```\n\n- **Dynamic Expression**: Uses JavaScript's map function to iterate over args.intents, an array of intent objects passed into the function. Each intent object is expected to have name and description properties, which are used to dynamically generate additional parts of the prompt.\n\n**Final Classification Challenge**\n\n```\nYou are given an utterance and you have to classify it into an action based on the description. Only respond with the action class. 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