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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  Written byTheo Grant

  Theo Grant explores real-world AI applications, automation workflows, and hands-on tutorials at AI In Action Hub. Theo breaks down complex AI concepts into practical guides that help professionals and creators leverage AI in their daily work.