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A second retrieval stage that re-scores an initial set of candidate passages with a more accurate model, pushing the most relevant ones to the top.
Retrieval often uses a fast first stage to fetch many candidates, then a slower, more accurate reranker to reorder them. Nogueira & Cho (2019) showed that a BERT cross-encoder — which reads the query and a passage together — substantially improves ranking quality over the first-stage retriever alone.
In RAG systems, reranking before passing context to the model improves answer quality by ensuring the most relevant passages are used.
Primary, peer-reviewed and archival sources for this definition.
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