Initial retrieval often prioritizes recall, returning a broad candidate set. Reranking uses a more query-aware scoring method to improve precision. It can be a cross-encoder, semantic ranker, or model-based scorer. It adds latency and cost and cannot recover a relevant document that never entered the candidate set.

A typical experiment retrieves broadly, merges and deduplicates, reranks, then passes a smaller top set to generation. Evaluate end-to-end answer quality and latency, not just ranking scores.