Lost in the Middle" Still Kills RAG Quality — Placement Matters

Stanford research has been widely validated in production: LLM performance degrades significantly when key information is buried in the middle of long contexts. For RAG pipelines, put the most important retrieved chunks at the start or end of the input, not sandwiched in the middle. Retrieval quality ultimately determines answer quality — wrong chunks in means confidently wrong answers out, regardless of model size.

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Sources: Stack AI – Prompt Engineering for RAG