GEO·AEOContent StrategyUpdated 2026.04.28

Chunking

Also known as청크 분할Document Chunking

In one line

Chunking is the practice of slicing long content into smaller units that LLMs can ingest cleanly — the same units that show up as citation passages in RAG and AI search.

Going deeper

Chunking originally came from LLM application work — RAG and similar systems. Long documents are not fed to the model whole; they are sliced into units (paragraphs, sections, fixed token windows), indexed, and retrieved. AI search engines do something similar to break pages down into citation-sized passages.

The direct GEO question is: does each chunk still make sense on its own? A page that reads well end-to-end can still produce broken chunks, and broken chunks drop out of the citation pool. Chunking and chunk optimization are really the same job seen from two angles.

One operating tip: align your heading structure with the chunk boundaries. The cleaner your H2/H3 signals, the more likely an LLM will slice the page along natural seams, which cuts down on awkward mid-thought splits.

Related terms

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