> ## Documentation Index
> Fetch the complete documentation index at: https://wiki.platelunchcollective.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Cross-encoder

> A cross-encoder is the model architecture used in reranking that takes a query-chunk pair as joint input and outputs a relevance score.

*Technical implementation* · *AI Search Infrastructure*

## Definition

A cross-encoder is the model architecture used in reranking. It takes a query-chunk pair as joint input — processing both together — and outputs a relevance score. Unlike a bi-encoder, it can attend to the interaction between query and chunk, making it dramatically more accurate at relevance scoring.

## Why It Matters for AI Search

Cross-encoders are too slow to run over a full index at query time, which is why retrieval pipelines separate first-pass retrieval (bi-encoder speed) from reranking (cross-encoder accuracy). The cross-encoder sees far fewer candidates — the top-k from first pass — and can afford to be thorough. Content that answers a query completely and specifically scores better under cross-encoder reranking than content that is merely topically adjacent.

## Related Terms

<CardGroup cols={2}>
  <Card title="Reranking" href="/ai-search-glossary/reranking" />

  <Card title="Bi-encoder" href="/ai-search-glossary/bi-encoder" />

  <Card title="First-pass retrieval" href="/ai-search-glossary/first-pass-retrieval" />

  <Card title="Retrieval pipeline" href="/ai-search-glossary/retrieval-pipeline" />

  <Card title="Cosine similarity" href="/ai-search-glossary/cosine-similarity" />
</CardGroup>

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