Originally published on AI Tech Connect.
What you need to know Reranking is a two-stage funnel. Retrieve a wide, cheap candidate set with fast retrievers, then re-score only that shortlist with an expensive-but-accurate reranker and keep the top few for the model. You get cross-encoder precision without running it over your whole corpus. The durable pattern in 2026 is lexical + dense → RRF → cross-encoder. BM25 catches exact terms, a dense bi-encoder catches meaning, Reciprocal Rank Fusion merges them into one pool of about 100 candidates, and a cross-encoder reranks to the top five to ten that reach the LLM. Bi-encoders are fast; cross-encoders are precise. A bi-encoder embeds query and document separately so vectors can be pre-indexed and searched over millions of documents. A cross-encoder reads query and document together…
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