> ## 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.

# Reranking

> Reranking is the second stage of a retrieval pipeline where the candidate set from first-pass retrieval is re-scored by a separate model.

*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.

## Related Terms

<CardGroup cols={2}>
  <Card title="Cross-encoder" href="/ai-search-glossary/cross-encoder" />

  <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="Context assembly" href="/ai-search-glossary/context-assembly" />
</CardGroup>

## Relevant Plate Lunch Collective Services

[Citation-Ready Content](https://www.platelunchcollective.com/services/citation-ready-content)  [AI SEO](https://www.platelunchcollective.com/services/ai-seo)