Agents & Tools · intermediate
Reranker (re-ranking, reranking)
A reranker is a second-pass scoring model that takes the top-K retrieved candidates and reorders them by joint relevance to the query. Typically a cross-encoder; dramatically improves retrieval precision at low cost.
Explanation
Vector retrieval is fast but imprecise — it scores each document independently of the query during indexing. A reranker takes (query, candidate) pairs and computes a joint score, catching nuances the bi-encoder embedding missed.
Typical pipeline: retrieve top 50 with vector + BM25 hybrid, then rerank to top 5 with a cross-encoder. The reranker runs ~50 forward passes — cheap compared to the LLM call that follows — and consistently lifts answer quality by 5-15 points on RAG benchmarks.
Common rerankers: Cohere Rerank (proprietary, very strong), BGE Reranker (open, small), Voyage Rerank, ColBERT (a different architecture entirely).
Adding a reranker is the single highest-leverage RAG improvement after fixing chunking.
Examples
- A hybrid-search RAG returns 50 candidates; Cohere Rerank trims to the 5 most relevant; faithfulness score jumps from 0.68 to 0.81.
- BGE-rerank-large-v2 used in an offline pipeline to clean up top-K results before LLM call.
When to use reranker
After chunking is sorted, before model upgrades. Reranking is consistently the best dollar-for-quality RAG investment.
Frequently asked
What is Reranker?
A reranker is a second-pass scoring model that takes the top-K retrieved candidates and reorders them by joint relevance to the query. Typically a cross-encoder; dramatically improves retrieval precision at low cost.
What is an example of reranker?
A hybrid-search RAG returns 50 candidates; Cohere Rerank trims to the 5 most relevant; faithfulness score jumps from 0.68 to 0.81.
How is Reranker related to Retrieval-Augmented Generation?
Reranker and Retrieval-Augmented Generation are both agents & tools concepts. RAG retrieves relevant documents from a corpus at query time and includes them in the prompt, letting an LLM answer with up-to-date, source-cited, private information without retraining.
When should I use reranker?
After chunking is sorted, before model upgrades. Reranking is consistently the best dollar-for-quality RAG investment.
Is Reranker considered intermediate?
Reranker is generally considered intermediate-level material in the AI and LLM space.