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Build a Custom RAG Chatbot on Your Own Documents – A Step-by-Step Tutorial

1. What Is RAG and Why You Need It

• Understand the core concept: Retrieval-Augmented Generation combines document search with LLM reasoning to answer questions based on your private data.
• Learn the key difference between a generic chatbot and a RAG-powered one — no fine-tuning required, just smart retrieval.
• Explore real-world use cases: internal knowledge bases, customer support, research assistants, and compliance Q&A.

2. Setting Up Your Environment and Tools

• Choose your tech stack: Python 3.10+, LangChain or LlamaIndex, OpenAI API (or a local model via Ollama), and a vector database like ChromaDB.
• Install all dependencies in a virtual environment and configure your API keys securely using environment variables.
• Prepare a sample dataset — a few PDFs, Markdown files, or plain text documents — to test the pipeline end-to-end.

3. Chunking and Embedding Your Documents