Chunking
Chunking is the preprocessing step where RAG slices a page into 200–500 word fragments for vector indexing — usually along H2/H3, paragraph, and sentence boundaries. Chunker-friendly writing means clear subheadings + complete paragraphs = clean citable blocks.
Category: Technical
What is Chunking?
Chunking is the preprocessing step where RAG slices a page into 200–500 word fragments for vector indexing — usually along H2/H3, paragraph, and sentence boundaries. Chunker-friendly writing means clear subheadings + complete paragraphs = clean citable blocks.
How do you apply it in practice?
Anti-patterns: critical info trapped in images or tables (chunker can't read them); orphan pronouns where 'it' has no antecedent.
Why does it matter for AI search citations?
Generative search engines tend to extract sentences that are clearly defined, consistent and quotable on their own. Writing concepts like "Chunking" as one consistent definition across your site, backed by structured data and verifiable sources, lowers the chance of being misread. This improves readiness only; it does not guarantee citations or ranking gains — measure actual citations to know.
Which related terms should you know?
RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) is the core architecture of answer engines: user query → vector-retrieve relevant pages → feed retrieved chunks to an LLM to synthesize the answer. This means content must be crawlable, chunkable, and embeddable.
Answer Capsule
An Answer Capsule is a 40–60 word direct-answer block placed within the first 200 words of an article, structured as 'definition + key action'. It is the highest-ROI GEO writing pattern because ChatGPT, Perplexity, and Google AI Overviews preferentially extract self-contained, copy-pasteable sentences as citations.
Chunking at a glance
| Term | Chunking |
|---|---|
| Category | Technical |
| Last reviewed | 2026-10-06 |
References
- Google Search Central: SEO documentation
- Schema.org structured data vocabulary
- W3C JSON-LD 1.1 Recommendation
- GEO: Generative Engine Optimization (arXiv paper)
These documents support the definitions and methods, not ranking or citation promises; reviewed as of 2026-10-06.
By the 23SEOGEO team · Last updated: