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A user searches your RAG system for the exact error string 'ORA-01555' and the one runbook that names that error does not appear in the top results, even though your vector search ran fine. What is the most likely cause?

Dense embeddings capture meaning but blur exact rare tokens, so the literal error code is a weak signal for vector search, This is the dense blind spot. An embedding places an exact rare token near vaguely related text and loses the precise match. Error codes, SKUs, names, and code identifiers are exact strings, not concepts, so vector search ranks them poorly. Keyword search, which matches the literal token, is what catches them, which is the case for hybrid retrieval.

Why does Reciprocal Rank Fusion combine a BM25 list and a vector list without rescaling their scores?

It ignores raw scores and uses only each document's rank position, scoring by the sum of 1 / (k + rank) across lists, RRF discards the raw scores, which live on different and incomparable scales (bounded cosine versus unbounded BM25), and keeps only rank position. Each document scores the sum over lists of 1 / (k + rank), with k defaulting to 60. Because it looks only at ranks, no calibration between the two systems is needed, which is why it is the common default.

In BM25, why does a rare term like an error code or a function name get a high weight?

Because inverse document frequency gives a large weight to terms that appear in few documents, so the one document containing the rare exact term rises to the top, Inverse document frequency is the lever. A term in almost every document carries little weight; a term in only a handful carries a lot. An error code, SKU, or function name is rare across the corpus, so BM25 weights it heavily and surfaces the document that contains it. This is exactly the signal an embedding smooths away.

You add hybrid retrieval in front of your cross-encoder reranker. Why does the order matter?

A reranker can only reorder the candidates retrieval gave it, so hybrid widens recall to make sure both the paraphrase match and the exact match are in the candidate set before reranking, Retrieval controls recall, which documents enter the candidate set. Reranking controls precision, the ordering of that set. A reranker cannot promote a document that never made it into the candidates. Hybrid retrieval widens recall so both the exact and paraphrase matches are present, then the reranker orders them. Hybrid feeds rerank; it does not compete with it.

Hybrid Retrieval: When Keyword Search Beats Your Embeddings

Name: System Design Masterclass
Availability: InStock

in Retrieval Rag

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