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Reranking is a second-stage retrieval step that reorders an initial set of candidate passages so the most relevant ones reach the model first, scoring each query-document pair directly rather than by precomputed vector distance.

The first stage (vector or Hybrid Search) is built for speed and recall over a large corpus and returns many roughly-relevant candidates. A reranker — typically a cross-encoder or an LLM — then reads query and passage together to judge true relevance, which is far more accurate but too slow to run over everything.

For builders it is a cheap precision boost for RAG: rerank the top fifty to a hundred hits down to the handful that fit the context window. The tradeoff is added latency and cost per query, so the candidate count is the dial to tune.

Topic: RAG Related: RAG Hybrid Search Embeddings

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FAQ

What is Reranking?

Reranking reorders retrieved candidates after an initial search step so the best evidence reaches the LLM first. GROUNDING tracks cross-encoders, LLM rerankers, latency tradeoffs, and RAG quality gains.

Which topic does Reranking belong to?

On the GROUNDING radar, Reranking is grouped under the RAG topic.

Related concepts tracked by the radar include RAG, Hybrid Search, Embeddings.