Reranking

Reranking is a two-stage retrieval technique that first retrieves a broad candidate set using a fast bi-encoder or keyword method, then re-scores those candidates with a slower, more accurate cross-encoder model to improve final ranking quality.

Reranking is a two-stage information retrieval technique used to improve the relevance of search and document retrieval results. In the first stage, a fast method—such as BM25 keyword matching or approximate nearest neighbor (ANN) vector search via bi-encoder embeddings—quickly retrieves a large candidate set, often 50–500 items, from a corpus. In the second stage, a reranker model re-scores each candidate against the original query and reorders them to surface the most relevant results at the top.

The core reranker is typically a cross-encoder: a transformer that receives the query and a candidate document concatenated as input, allowing full cross-attention between them. This is computationally expensive compared to computing embeddings independently, but produces significantly higher-quality relevance scores. Widely used reranker models include Cohere Rerank 3 (released April 2024), BAAI's BGE-Reranker-v2 series, and ColBERT-based models that use late interaction between token-level embeddings as a middle ground between speed and accuracy.

Reranking is a critical component in Retrieval-Augmented Generation (RAG) pipelines, where only the top-k re-ranked passages are passed to the language model as context. Without reranking, marginally relevant chunks can displace genuinely useful ones, degrading generated answer quality. The same pattern applies in enterprise search, legal document discovery, and customer support systems that demand high precision.

As of 2026, reranking is a standard building block in production RAG architectures. Hosted APIs from Cohere, JinaAI, and Voyage AI make it straightforward to add reranking without self-hosting model infrastructure, and frameworks such as LangChain and LlamaIndex provide native integrations. Research continues into learned sparse retrieval and late-interaction models that blur the boundary between first-stage retrieval and reranking.