The length of text segments stored in a vector index.
1 min readupdated 2026-07-04
/ quick answer
Chunk size trades off recall and precision. Small chunks (200-400 tokens) return tight, on-topic snippets but lose context; large chunks (800-1500) keep context but dilute similarity scores. The length of text segments stored in a vector index.
The length of text segments stored in a vector index. Chunk size trades off recall and precision. Small chunks (200-400 tokens) return tight, on-topic snippets but lose context; large chunks (800-1500) keep context but dilute similarity scores. In practice: A legal RAG uses 500-token chunks with 100-token overlap so clauses stay whole. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Definition
Chunk size trades off recall and precision. Small chunks (200-400 tokens) return tight, on-topic snippets but lose context; large chunks (800-1500) keep context but dilute similarity scores.
Example
A legal RAG uses 500-token chunks with 100-token overlap so clauses stay whole.
Chunk size trades off recall and precision. Small chunks (200-400 tokens) return tight, on-topic snippets but lose context; large chunks (800-1500) keep context but dilute similarity scores.
What is an example of Chunk Size?
A legal RAG uses 500-token chunks with 100-token overlap so clauses stay whole.
Why does Chunk Size matter for AI and automation?
The length of text segments stored in a vector index. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.