Splitting documents into retrievable passages
Chunking splits a long document into units that can be indexed and retrieved. A passage that is too short loses context; one that is too long mixes topics and reduces precision.
What Chunking means in GEO
In a RAG pipeline, publishing is not enough. A page must be discoverable, enter the candidate set and survive reranking.
Why it matters
Design self-contained sections with a heading, answer, evidence and enough references to understand them independently.
Identify the stage where the source disappears: access, initial retrieval, reranking, generator context or citation selection.
How it connects to other concepts
Chunking belongs to retrieval and rag. It is best analyzed alongside related terms because AI visibility depends on several stages and signals rather than one isolated optimization.
Common mistake
Blaming final copy when the document never reached the model that writes the answer.
Turn these concepts into metrics
Bee LLM tracks mentions, citations, position, sentiment and competitors across major AI engines.