# Reranking

> Reranking is a second-pass scoring step: a cross-encoder model re-orders the top results from fast retrieval so the truly relevant few rise to the top.

**Reranking is the precision stage of retrieval: after a fast first pass fetches candidate documents, a reranker model scores each candidate against the query directly and re-orders them, so the few results that actually matter end up on top.**

The two stages exist because of an accuracy/speed trade. First-pass retrieval ([semantic](/glossary/semantic-search) or keyword) uses representations computed independently — fast enough for millions of documents, but blind to fine query–document interaction. A reranker is a **cross-encoder**: it reads the query and candidate *together*, which is dramatically more accurate and dramatically slower — viable only on a short list. The standard [RAG](/glossary/rag) pattern: retrieve top-50 cheaply, rerank to top-5 precisely, and put just those in the prompt — better answers *and* fewer tokens.

Hosted rerankers ([Cohere Rerank](/tools/cohere-rerank), [Voyage](/tools/voyage-ai)) make the step one API call. Whether it pays in *your* pipeline is an empirical question — [Hybrid Search & Reranking](/guides/concepts/hybrid-search-reranking) covers the architecture, and the [benchmark-rerankers](/commands/review/benchmark-rerankers) command measures the lift on your own queries.

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_Source: https://agentscamp.com/glossary/reranking — Term on AgentsCamp._