What Is a Reranker? How Second-Stage Models Improve Search and RAG Pipelines

This explainer covers rerankers — the second-stage retrieval models that re-score and reorder candidate documents after an initial fast retrieval step — and why they matter for building high-quality search and retrieval-augmented generation (RAG) systems. Unlike bi-encoder models that embed documents and queries independently, rerankers use cross-attention to jointly evaluate query-document pairs, producing significantly more accurate relevance scores at higher computational cost. For developers building RAG applications, adding a reranker between vector search and LLM context injection is one of the highest-ROI improvements available, often dramatically reducing the number of irrelevant chunks passed to the model. The piece covers practical tradeoffs including latency, when to use rerankers versus late interaction models like ColBERT, and common open-source options such as Cohere Rerank and BGE reranker models. Understanding this component is increasingly essential for anyone building production-grade search or document QA systems.