> ## 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.

# Retrieval Pipeline

> A retrieval pipeline is the sequence of steps an AI system takes to find, rank, and return relevant content in response to a query — including query embeddin...

*Technical implementation* · *AI Search Infrastructure*

## Definition

A retrieval pipeline is the sequence of steps an AI system takes to find, rank, and return relevant content in response to a query — including query embedding, vector search, re-ranking, and passage extraction before final answer synthesis.

## Why It Matters for AI Search

The retrieval pipeline is the path content must travel to become a citation. Content that is indexed, properly embedded, semantically relevant to the query, and structured for extraction has a chance to complete the pipeline. Content that fails at any step — not indexed, poorly embedded, semantically vague, or structurally opaque — drops out. Understanding the pipeline helps prioritize which optimization efforts matter most: technical indexability first, semantic relevance second, structural extractability third.

## Related Terms

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

  <Card title="Dense retrieval" href="/ai-search-glossary/dense-retrieval" />

  <Card title="Vector database" href="/ai-search-glossary/vector-database" />

  <Card title="Chunking" href="/ai-search-glossary/chunking" />

  <Card title="Re-ranking" href="/ai-search-glossary/re-ranking" />
</CardGroup>

## Relevant Plate Lunch Collective Services

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