Retrieval Reranking
A two-stage search process where an initial retrieval step finds candidate results (using keyword or vector search) and a reranking model scores them by relevance to the specific query. Reranking dramatically improves search quality for AI agents: the first stage is fast but approximate, the reranker is slower but precise. Support agents use it to find the most relevant KB article; legal agents use it to surface the most pertinent precedent.
Written by Max Zeshut
Founder at Agentmelt · Last updated Oct 26, 2026
Example
A support agent searches 10,000 KB articles. Vector search retrieves the top 50 candidates in 20ms. A cross-encoder reranker scores all 50 against the customer's exact question in 100ms, surfacing the single best article—far more accurate than vector search alone.
Frequently asked questions
- Is reranking worth the extra latency?
- Almost always, yes. Reranking adds 50–200ms but typically improves answer accuracy by 15–30%. For support agents, this means fewer escalations. For legal agents, it means citing the right precedent. The latency cost is negligible compared to the quality gain.