Reranking in RAG: How to Fix Bad Retrieval with a Reranker
This guide explains that vector search often ranks near-miss chunks first because embeddings compress whole chunks into single vectors, and shows how adding a cross-encoder reranker over the top 30 candidates raised precision@1 from 81% to 97% on a 274-question synthetic benchmark at roughly $0.10 per 1,000 queries. It compares 11 rerankers (voyageai/rerank-3-lite and rerank-3 were the most accurate dedicated models and cheapest, while LLM rerankers like openai/gpt-6-luna hit 99.6% but took about 4 seconds per call), covers candidate-count tuning, input_type settings, and silent failures such as cohere/rerank-v3.5's 4,096-token limit. It includes a Python example using the embeddings and rerank APIs and notes that real-world gains depend on your own documents and evaluation set.
Why it matters: Offers a measured, benchmark-backed walkthrough of adding reranking to a RAG pipeline, including cost, latency and failure-mode comparisons useful for teams tuning retrieval.