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Retrieval
Cross-encoder reranker
A small model that re-scores retrieved chunks for relevance to the query.
After initial retrieval returns 20-50 candidate chunks, a cross-encoder reranker scores each (query, chunk) pair jointly and surfaces the top 3-5 to send to the LLM. Rerankers are slower than vector search but dramatically more accurate, because they let the model attend to query and document together. Cohere Rerank, Jina Rerank, and BGE Rerank are common choices. A reranker is the single highest-leverage retrieval upgrade.
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