Reranking

Retrieval systems face a fundamental tension: the models that produce the most accurate relevance judgments are too slow to score millions of documents at query time, while the models fast enough for first-stage retrieval are less precise. The two-stage architecture resolves this by using a fast approximate retriever (ANN vector search, BM25, or hybrid) to narrow the candidate set to the top 50–200 results, then applying a reranker to this small candidate set to produce a final, high-quality ranking that is passed to the LLM or presented to the user.

Cross-encoder rerankers differ architecturally from bi-encoder embedding models. Where a bi-encoder independently encodes the query and each document into separate vectors, a cross-encoder processes the query and a candidate document jointly through a transformer, enabling full cross-attention between query and document tokens. This joint processing captures fine-grained relevance signals (term co-occurrence, negation, specificity) that bi-encoders miss, but is prohibitively expensive to apply to a full corpus. Rerankers are therefore always applied to a pre-filtered candidate set.

For enterprise RAG pipelines, reranking is one of the highest-leverage optimizations available. A retrieval pipeline that returns the top-K semantically similar chunks to an LLM benefits enormously from reranking because LLMs have limited context windows: irrelevant chunks in the context dilute attention and degrade answer quality. Reranking ensures that the fixed context window is filled with the most relevant passages available. Enterprise teams should also consider reranking as a quality gate for compliance-sensitive applications, as it provides an additional layer of relevance filtering before content is surfaced to end users.