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
Semantic Chunking
Semantic Chunking splits content along meaning boundaries rather than fixed lengths — preserving context inside each passage and producing GEO-friendly citation units.
GEO·AEOChunk Optimization
Chunk optimization is the practice of structuring content into self-contained passages an LLM can lift directly into its answer.
LLMRAG
RAG (Retrieval-Augmented Generation) lets an LLM fetch external documents at answer time and ground its response in them — the technique behind ChatGPT Search, Perplexity and most AI search products.
GEO·AEOCitation-worthiness
Citation-worthiness is how readily a piece of content gets quoted by an LLM — driven mainly by factual specificity, self-containment, structure and authority.
GEO·AEOGEO
GEO (Generative Engine Optimization) is the practice of optimizing content and data so that a brand gets cited and recommended inside generative AI search answers like ChatGPT, Perplexity and Google AI Overviews.
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