Content chunking
Chunking is writing sections that still make sense if a retriever lifts 100–300 words and ignores the rest of the page.
RAG systems do not “read the article.” They grab passages. If the passage is a joke plus a CTA, you get cited as a joke.
Each H2 should answer something. Dates and nouns in the chunk help. Pronouns that point at a previous section do not survive extraction.
Examples
- A section titled “Battery life” that starts with “It lasts 14 hours in our lab test on 13 January 2026” is a good chunk. “As mentioned above, performance is strong” is a bad one.
Related terms
Pages that lead with a direct answer, then proof. Built for snippets, Overviews, and chat extraction.
Breaking ideas into reusable factual units that can be retrieved alone: a definition, a number, a limit, a date.
RAG means the model looks up documents at answer time instead of relying only on what it memorized in training.
Retrieving a passage instead of a page. The unit of AI visibility is often the chunk.
FAQ
Is there an ideal chunk size? +
Depends on the index. Write for humans first, but make each section self-contained. That survives most splitters.
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