Splitting documents into retrievable pieces before embedding them for RAG.
1 min readupdated 2026-06-22
/ quick answer
Chunking decides what unit of text your retriever returns. Too small → loses context. Too big → wastes tokens and dilutes relevance. Common strategies: fixed-size, sentence, semantic, and structural (by heading/section). Splitting documents into retrievable pieces before embedding them for RAG.
Splitting documents into retrievable pieces before embedding them for RAG. Chunking decides what unit of text your retriever returns. Too small → loses context. Too big → wastes tokens and dilutes relevance. Common strategies: fixed-size, sentence, semantic, and structural (by heading/section). In practice: A 40-page PDF split into ~800-token overlapping chunks by markdown heading typically outperforms naive 500-char splits on Q&A accuracy. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Definition
Chunking decides what unit of text your retriever returns. Too small → loses context. Too big → wastes tokens and dilutes relevance. Common strategies: fixed-size, sentence, semantic, and structural (by heading/section).
Example
A 40-page PDF split into ~800-token overlapping chunks by markdown heading typically outperforms naive 500-char splits on Q&A accuracy.
Chunking decides what unit of text your retriever returns. Too small → loses context. Too big → wastes tokens and dilutes relevance. Common strategies: fixed-size, sentence, semantic, and structural (by heading/section).
What is an example of Chunking?
A 40-page PDF split into ~800-token overlapping chunks by markdown heading typically outperforms naive 500-char splits on Q&A accuracy.
Why does Chunking matter for AI and automation?
Splitting documents into retrievable pieces before embedding them for RAG. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.