Chunking
Breaking content into short, self-contained passages so AI systems can retrieve and cite exactly the right section.
Plain Language Definition
Chunking is the process of cutting a long document into short, self-contained paragraphs so the AI can easily extract precise facts from exactly the right section. A well-chunked document answers one question per paragraph, making each passage independently meaningful. Poorly chunked content with long rambling paragraphs gets retrieved as a less precise blob.
Technical Definition
Algorithmic segmentation of documents into discrete semantic units (typically 40–200 word passages) optimized for vector database storage limits, retrieval precision, and context-window injection — with each chunk maintaining semantic self-sufficiency.
Why This Matters for AI Search Visibility
RAG systems retrieve at the chunk level, not the document level. If your most valuable fact is buried in a long unfocused paragraph, it will be retrieved alongside irrelevant context, reducing precision. Well-chunked content with one idea per block gets retrieved more cleanly and cited more accurately.