# Chunking

> Splitting a document into smaller passages so each can be embedded, indexed and retrieved independently in a RAG pipeline.

_The Agentic Web Lexicon · /glossary/chunking · [JSON](/api/glossary/chunking) · [all The Agentic Web Lexicon](/glossary)_

- **term:** Chunking
- **category:** knowledge-memory
- **short_def:** Splitting a document into smaller passages so each can be embedded, indexed and retrieved independently in a RAG pipeline.
- **long_def:** Chunking decides the unit of retrieval: documents are cut into passages (by size, by heading, or semantically) before embedding, so a query returns the few most relevant chunks rather than a whole document. Chunk size is a tradeoff — too large dilutes relevance, too small loses context.
- **see_also:** rag, embeddings, reranking
- **etymology_origin:** — (verify-against-primary-at-build)
- **related_to:** rag, embeddings, reranking, vector-database
- **contrast_with:** Unlike indexing a whole document as one unit, chunking retrieves passage-level pieces, so an agent gets the relevant paragraph instead of the entire file.
- **example:** A 40-page manual chunked by section lets a query return just the two paragraphs that answer it, keeping the agent's context window focused.
- **source:** https://en.wikipedia.org/wiki/Retrieval-augmented_generation
- **status:** active
- **why_it_matters:** How you chunk determines what an agent retrieves; clean headings and self-contained sections (the same structure that makes content answer-first) chunk cleanly and lower Cost of Retrieval.
- **sameAs:** https://en.wikipedia.org/wiki/Retrieval-augmented_generation
- **bridge_entity:** rag
- **last_verified:** 2026-07-06
- **md_twin:** /glossary/chunking.md