ModelRefs / Chunking — AI Glossary
Chunking — AI Glossary
Splitting source documents into smaller segments before embedding for RAG retrieval. Chunk size affects both retrieval quality and context utilization.
Overview
Chunk size affects both retrieval quality and context utilization. Fixed-size (512–1024 tokens) is simple. Semantic chunking splits on content boundaries for better coherence. Sliding window adds overlap. Parent-document retrieval uses small chunks for search but returns larger context.
Reference details
| Topic | rag |
|---|---|
| Last reviewed | 2026-06-24 |
Related terms
Commonly confused with
The operation; a text splitter is the component that performs it, the way memory relates to a memory store. Chunk size and overlap are its parameters, and they are the two settings most often blamed for RAG quality when the real cause is elsewhere. Worth measuring against retrieval metrics rather than tuned by feel, since the effect of a size change is not visible in the generated answer.
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Frequently asked questions
What is Chunking?
Splitting source documents into smaller segments before embedding for RAG retrieval.
What concepts are related to Chunking?
Closely related concepts include rag, embedding, semantic search.