How to Build a Custom AI Chatbot with Your Own Data Using RAG
1. What Is RAG and Why It Matters for Your AI Chatbot
- Define Retrieval-Augmented Generation (RAG) and how it combines retrieval of external data with LLM generation.
- Explain the core benefit: your chatbot can answer questions based on your proprietary documents, not just public training data.
- Highlight real-world use cases — customer support, internal knowledge bases, research assistants.
2. Prerequisites: Tools, Libraries, and Data Preparation
- List required tools: Python 3.10+, LangChain, ChromaDB or Pinecone, an LLM API (OpenAI, Cohere, or open-source via Ollama).
- Show how to prepare your data — clean PDFs, markdown files, or CSV exports; splitting
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