RAG Systems That Actually Retrieve the Right Thing
Retrieval-augmented generation looks simple in a demo and falls apart in production. The details that make the difference.
Retrieval-augmented generation has a deceptively simple pitch: embed your documents, retrieve the relevant ones, hand them to the model. The gap between that pitch and a system that reliably answers real questions is almost entirely in the retrieval half, not the generation half.
Chunking strategy, metadata filtering, and query rewriting usually matter more than which embedding model you pick. A system that retrieves the wrong three paragraphs will produce a confident, well-written, wrong answer — and that failure mode is far more dangerous than an obvious error.
The systems that hold up in production treat retrieval quality as a metric to be measured continuously, with a labeled evaluation set of real queries and expected sources — not something you eyeball once during a demo and assume keeps working as your document set grows.
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