Reranking in RAG: How to Fix Bad Retrieval with a Reranker

Reranking improves Retrieval-Augmented Generation systems by reordering the candidate document chunks returned by vector search with a cross-encoder that evaluates each chunk directly against the user’s question, helping distinguish close topical matches such as different subscription-plan limits or similar error codes. In a controlled 274-question synthetic benchmark, retrieving 30 candidates and reranking them with Voyage models raised precision@1 from 81.0% with Google Gemini embeddings alone to roughly 97%, while sending the reranked top three chunks to an LLM improved answer accuracy to 99.3% and reduced the amount of context required. VoyageAI’s rerank-3 and rerank-3-lite offered the strongest dedicated-model balance of accuracy, latency, and cost in the test, while LLM-based rerankers achieved up to 100% precision but were slower and generally more expensive. The recommended approach is to retain embedding search as the initial retrieval stage, use correct query and document embedding input types, rerank approximately 30 candidates, and evaluate outcomes with real application questions rather than relying only on recall metrics. Reranking cannot recover documents absent from the initial candidate set and may fail when chunks exceed a model’s context limit, so chunk sizing, filtering low-quality material such as unanswered forum posts, candidate-count tuning, and end-to-end evaluation remain important.

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