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.

Retrieval Reranking

Written by Max Zeshut

Founder at Agentmelt · Last updated Sep 9, 2026

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.

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.

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