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Technical implementation · AI Search Infrastructure

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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. 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.

Cross-encoder

Bi-encoder

First-pass retrieval

Retrieval pipeline

Context assembly

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