Chunking (Retrieval)

Chunking is how AI reads at scale. Before content can be retrieved and cited, long pages are broken into chunks — passages sized so a model can grab exactly the relevant piece without dragging in the whole document. Retrieval systems then embed and rank chunks, not entire articles.

The strategic takeaway is concrete: structure decides retrievability. Content built in clean, self-contained sections — clear headers, one idea per passage, answers that stand on their own — chunks well and surfaces often. Walls of undifferentiated text chunk badly and get lost.

Writing for chunking is a core Generative Engine Optimization discipline. Every section should be liftable and citable on its own. If a passage only makes sense in the context of the whole page, an answer engine can't use it.