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Technical implementation · AI Search Infrastructure
Definition
Reranking is the second stage of a retrieval pipeline where the candidate set from first-pass retrieval is re-scored by a separate model — typically a cross-encoder — that reads the query and each candidate chunk together and outputs a relevance score. The top-scoring chunks proceed to context assembly.
Why It Matters for AI Search
The reranker is where most of the “why did that get cited and not this” behavior actually lives. First-pass retrieval gets candidates into the room using approximate vector similarity. The reranker reads each candidate carefully against the specific query and makes a precision judgment. A chunk that retrieves in the first pass but scores poorly in reranking does not appear in the final context — and therefore cannot be cited.
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