Why RAG Docs Chatbots Answer Wrong: Embeddings, Chunking, and Context Fixes

Short summary

A practical guide to fixing wrong answers in RAG document chatbots by treating retrieval as an evaluated evidence pipeline rather than relying on fluent generation. The author recommends measuring retrieval recall before judging prose quality, reranking candidates, and counting tokens before the chat request. Key insight: most RAG failures are retrieval misses, not generation model problems, so the evidence path deserves attention before swapping models.

• •Treat RAG retrieval as an evaluated evidence pipeline, not just a prompt engineering problem
• •Log expected source passages, top retrieved chunks, ranks, and final answers to diagnose different failure types separately
• •Use source-only generation with reranking and token budgeting; let the model say 'not found' for unsupported questions

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