> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.vellum.ai/product/workflows/examples/basic-rag-chatbot/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.vellum.ai/_mcp/server.

# RAG Chatbot

> Build a powerful chatbot that leverages Retrieval Augmented Generation (RAG) to provide accurate, context-aware responses based on your documents and knowledge base.

Concepts: Document Indexes, RAG, Chatbot, Chat History, Vector Search, Knowledge Base Integration

This example shows how to create an intelligent chatbot that combines the power of large language models with your own knowledge base. By implementing RAG (Retrieval Augmented Generation), the chatbot can provide accurate, contextually relevant responses based on your documents while maintaining natural conversational flow.

> Build a powerful chatbot that leverages Retrieval Augmented Generation (RAG) to provide accurate, context-aware responses based on your documents and knowledge base.