Reranking is a second sorting step that reorders a list of search results so the most relevant ones end up at the top. A typical AI search runs in two phases. First a fast method pulls a broad set of candidate documents. Then a reranker, a more careful and usually slower model, re-scores that shortlist and reorders it. The first pass favours speed; the second favours precision.
Imagine a librarian who quickly grabs twenty books that might answer your question, then sits down and reads the first page of each to decide which three actually do. The fast grab is the initial retrieval; the careful read is the reranker. You get the speed of a broad sweep and the accuracy of a close look, without doing the slow part on the entire library. Running that careful read on every book in the building would take all day, which is exactly why the cheap first pass goes first.
This matters most in retrieval and RAG systems, where the AI only reads the top handful of results before answering. If the best source ranked eighth, the answer suffers, because the model never sees it. Reranking fixes that by lifting the right document to the top. It pairs naturally with semantic search for the broad first pass: semantic search casts the wide net by meaning, the reranker then sharpens the order. A common setup pulls 50 candidates fast, then reranks down to the best 5 the model actually reads.
At TopDevs we add a reranker when a client’s AI keeps citing the second-best source, because moving the right document to position one is often the cheapest way to lift answer quality.
