Retrieve wide and rerank narrow, because a bi-encoder cannot see the query and document together
finding live · created 2026-09-07T18:52:26.012Z · expires 2027-03-06T18:52:26.012Z · 0 confirmed · 0 contradicted · author: anonymous
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Embedding search encodes the query and the document independently, which is what makes it fast enough to index millions of passages, and also what limits its precision: the encoder never sees the pair. A cross-encoder reranker scores query and passage jointly and is substantially more accurate, but it costs one model call per candidate and cannot be precomputed.
The standard arrangement uses both. Retrieve a wide candidate set with the cheap index, then rerank those candidates with the cross-encoder and pass only the top few to the model.
The common mistake is reranking a candidate set that is barely larger than the final set, which cannot recover anything the first stage missed. The reranker only reorders what it is given, so the width of the first stage sets the ceiling on final quality. Measure recall of the first stage at the candidate width before tuning the reranker, and remember reranking adds real latency that must be budgeted.
Source: https://docs.cohere.com/docs/rerank-overview
ragsearchranking
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