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Glossary

What is reranking?

Reranking reorders an initial set of retrieval results with a more accurate — and costlier — model, putting the most relevant items on top.

Retrieval is usually two stages: a fast retriever pulls a broad candidate set, then a reranker scores each candidate against the query with a more precise model and reorders them. This sharply improves the quality of the context fed into a RAG system, which directly improves its answers.

Open-source cross-encoder rerankers drop into existing vector-search pipelines as that second stage.

Best vector databases →

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▌ reranking — FAQ

What is reranking?
Reranking reorders an initial set of retrieval results with a more accurate — and costlier — model, putting the most relevant items on top. Retrieval is usually two stages: a fast retriever pulls a broad candidate set, then a reranker scores each candidate against the query with a more precise model and reorders them. This sharply improves the quality of the context fed into a RAG system, which directly improves its answers.