How Evaluated Retrieval Pipelines Fix Hallucinations in RAG Documentation Chatbots

RAG-based documentation chatbots frequently produce wrong answers not because of poor language generation, but because the retrieval stage fails to surface the correct passage before generation begins. Embedding search can return topically related text without returning the passage that actually answers a query, and improper chunking further degrades retrieval precision. A practical remedy involves measuring retrieval recall separately from answer quality, reranking candidate chunks, and enforcing token-budget discipline before passing context to the language model. The model should be explicitly instructed to respond with 'not found' when supporting evidence is absent, preventing it from filling gaps with general knowledge. Structured evaluation that tracks expected source passages, retrieved chunk ranks, and final answers allows engineers to isolate whether failures stem from chunking, retrieval ranking, or generation instructions.