What Is Reranking?
Reranking is a technique that improves search and retrieval quality by applying a second, more sophisticated relevance model to an initial set of retrieved results. In a typical pipeline, a fast but approximate first-stage retriever (like vector similarity search) pulls a broad set of candidates, and then a reranker carefully evaluates each candidate against the original query to produce a more accurate relevance ordering. This two-stage approach combines the speed of approximate retrieval with the precision of detailed relevance scoring.
The reranker sees both the query and each candidate document together, enabling it to assess fine-grained relevance that embedding similarity alone might miss.
Why Reranking Improves Results
First-stage retrievers based on embedding similarity are fast but imperfect. They sometimes surface results that are topically related but do not actually answer the query, or miss subtle relevance signals in longer documents. Rerankers, typically cross-encoder models, process the query and document together through a transformer, capturing nuanced interactions between query terms and document content that independent embeddings cannot represent.
In practice, reranking consistently improves retrieval metrics by 5-15% or more, which translates to noticeably better AI responses in RAG applications.
Implementation in RAG Pipelines
Place the reranker between retrieval and generation: retrieve a larger initial set (50-100 candidates), rerank, then pass the top results to the language model. This improves quality without changing the generation model. Balance reranking quality against latency — cross-encoder models are slower than embedding lookups, so optimize batch sizes and consider model distillation for latency-sensitive applications. Evaluate rerankers on your domain-specific queries, as performance varies across domains. Combine reranking with metadata filtering and diversity controls for the best results. Monitor reranker performance over time as your content and query patterns evolve.