Reranking

Reranking is a two-stage retrieval technique where an initial set of candidate documents is retrieved cheaply (by vector similarity or BM25) and then scored by a more accurate but expensive cross-encoder model to identify the most relevant passages. Cross-encoders jointly encode the query and document, capturing fine-grained relevance signals missed by bi-encoder models.

Reranking significantly improves RAG retrieval quality.