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How to Build a Custom AI Chatbot with Retrieval-Augmented Generation (RAG): A Step-by-Step Tutorial

1. What Is RAG and Why It Matters for Your Chatbot

• Define Retrieval-Augmented Generation and how it combines vector search with LLM response generation.
• Explain the key advantage: grounding answers in your own data (documents, FAQs, knowledge bases) to reduce hallucinations.
• Outline real-world use cases — customer support bots, internal knowledge assistants, and research helpers.

2. Prerequisites & Environment Setup

• List required tools: Python 3.10+, OpenAI API key (or any LLM provider), and a vector database like ChromaDB or Pinecone.
• Walk through installing dependencies: langchain, chromadb, openai, and p
  

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