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Why RAG gives wrong answers (and how to fix retrieval failures)
TL;DR: A look at common RAG failures when retrieval returns incomplete or low-coverage evidence, causing correct documents to be underrepresented in context. The post analyzes two failure modes and emphasizes retrieval quality over model faults.
The author demonstrates that RAG failures often stem from retrieval, not the model. In one failure, chunks were too small, causing important numeric details about expenses to be excluded from the retrieved context. Even when the system found relevant documents, the specific stipend and budgets weren’t included in the top results. The issue wasn’t hallucination but incomplete evidence due to overly granular chunking, leading to incorrect or incomplete answers. The broader takeaway is to tune chunking and retrieval to ensure critical details surface in context.
Question for the room: What retrieval/chunking strategy have you found most effective for preserving numeric details and policy specifics in RAG pipelines?
— via dev.to
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