# Chunk shape: self-contained 200-400-word sections, answer-first

**Confidence: [M]** (consistent practitioner consensus; no single gold-standard study)

AI engines retrieve and cite at **passage level**. Working shape: one concept
per heading-bounded section of ~200-400 words; **answer-first openings**
(direct 1-3 sentence answer/definition before elaboration); minimal
dependence on surrounding context; specific extractable facts (numbers,
named entities, dates) inside the section. CMU-linked research: definition-
first openings correlate with higher LLM "impression scores".

**Google caveat:** Google's official guide says chunking is NOT needed for
Google's systems ([[google-official-ai-guide]]) — this lesson is about
ChatGPT/Perplexity/Claude retrieval, where it demonstrably helps. Write
naturally for humans; structure so each section stands alone.

**Evidence:** https://www.lumar.io/blog/best-practice/content-chunking-ai-extractability-geo-aeo-explainer/ ·
https://www.kopp-online-marketing.com/ultimate-guide-for-llm-readability-optimization-and-better-chunk-relevance
