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How to Build a RAG-Powered Q&A Bot for Your Documentation in 30 Minutes

1. What Is RAG and Why You Need It

• Understand the core idea: Retrieval-Augmented Generation (RAG) combines document search with LLM reasoning to answer questions based on your own data.
• Learn the key benefits — no fine-tuning required, hallucination reduction, and the ability to update knowledge without retraining the model.
• See a real-world use case: turning a 200-page product

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