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RAG Architecture: From Basics to Production
Retrieval-Augmented Generation is the most common GenAI system design topic. Master chunking strategies, embedding models, vector databases, hybrid search, reranking, advanced retrieval patterns (HyDE, RAPTOR), agentic RAG, guardrails, and production evaluation with RAGAS.
Why RAG Exists
LLMs have two fundamental limitations:
- Knowledge is frozen at training cutoff — they can't answer questions about events after their training data ends
- No access to private data — your company's internal docs, customer data, and codebase are invisible to them
RAG solves both by retrieving relevant documents at query time and injecting them into the prompt as context. This lets you build "ChatGPT for your company docs" without retraining the model.
Key insight: RAG is a retrieval + generation problem. Most failures are retrieval failures (wrong chunks retrieved), not generation failures. If you give the LLM the right context, it almost always produces a good answer.