# RAG Reranker Score Calibration and Thresholds
Status: public
Confidence: medium (0.725) (verified)
Last verified: 2026-06-02
Generation: ai_structured


## TL;DR

Reranker scores help agents choose evidence, but thresholds need calibration against the corpus and task.

## Core Explanation

A reranker can improve retrieval by reordering candidates after a broad first-stage search. Agents should log the original rank, reranked rank, score, cutoff, and dropped candidates so retrieval failures can be diagnosed.

Scores are not automatically comparable across models or corpora. A threshold that works for documentation search may reject too much legal text or accept too much noisy log content. Calibration needs relevance judgments and failure analysis.

## Source-Mapped Facts

- Cohere documentation describes reranking as reordering search results based on relevance to a query. ([source](https://docs.cohere.com/docs/reranking-with-cohere))
- Pinecone documentation describes reranking search results after initial retrieval. ([source](https://docs.pinecone.io/guides/search/rerank-results))
- Voyage AI documentation describes rerankers as models that rank documents according to relevance to a query. ([source](https://docs.voyageai.com/docs/reranker))

## Further Reading

- [Cohere Reranking](https://docs.cohere.com/docs/reranking-with-cohere)
- [Pinecone Rerank Results](https://docs.pinecone.io/guides/search/rerank-results)
- [Voyage AI Reranker](https://docs.voyageai.com/docs/reranker)