> ## 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.

# Chunking

> Chunking is the process of breaking a large document into smaller, discrete segments before storing them in a vector database or retrieval system.

*Technical implementation* · *AI Search Infrastructure*

## Definition

Chunking is the process of breaking a large document into smaller, discrete segments before storing them in a vector database or retrieval system. Each chunk is embedded independently and retrieved as a unit when the chunk's meaning matches a query.

## Why It Matters for AI Search

How a document is chunked determines what gets retrieved. A chunk that contains a complete, self-contained answer is more likely to be retrieved and cited than a chunk that cuts a sentence in half or buries an answer in the middle of a paragraph. For content strategists, chunking is the retrieval-layer explanation for why self-contained paragraphs and clear heading structure matter — the structural choices that make content readable for humans also make it chunk-friendly for AI systems.

## Related Terms

<CardGroup cols={2}>
  <Card title="Vector database" href="/ai-search-glossary/vector-database" />

  <Card title="Embedding" href="/ai-search-glossary/embedding" />

  <Card title="RAG" href="/ai-search-glossary/rag" />

  <Card title="Self-contained paragraph" href="/ai-search-glossary/self-contained-paragraph" />

  <Card title="Content extractability" href="/ai-search-glossary/content-extractability" />
</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)