What Is Reranking?

Reranking is a second step in a search or retrieval pipeline that reorders an initial set of results by relevance. A fast first pass gathers a batch of candidate results, then a more precise but slower model re-scores them against the query and moves the best matches to the top.

How reranking works

Reranking splits retrieval into two stages for speed and quality. The first stage uses a fast method, such as vector similarity search, to pull a manageable set of candidates from a large collection, say the top fifty. That method is quick but only roughly accurate. The second stage runs a reranker, a model that looks at the query and each candidate together and scores how well they actually match, then sorts by that score.

Because the reranker examines each query-candidate pair closely, it judges relevance more accurately than the first pass, but it is too slow to run over an entire collection. Applying it only to the shortlist gets most of the accuracy at a fraction of the cost.

Why reranking matters for AI

Reranking matters because the quality of what a system retrieves sets a ceiling on the answers it can give. In retrieval-augmented generation, feeding a model the most relevant passages, rather than roughly related ones, leads to better, better-grounded answers. Reranking is a practical way to sharpen retrieval without searching the whole collection precisely. At Custom AI Studio, reranking is one of the techniques we use to make retrieval systems surface the most relevant material from a client's data.