Content structure best practices for LLMs

Core principle: write for retrieval

AI engines in search mode (ChatGPT Search, Perplexity, AI Overviews) work in two steps: a retrieval stage that pulls relevant passages from a corpus, then a generation stage that synthesises a response while citing those passages. Optimising for retrieval means making each of your paragraphs readable out of context.

Chunking: the granularity that matters

Retrieval systems split documents into chunks of a few hundred to a few thousand characters. Chunk boundaries often follow HTML structure (headings, paragraphs).

HTML component	Role in chunking	Best practice
H2	Hard boundary	One H2 = one distinct intent, with its implicit long-tail query.
H3	Secondary boundary	Sub-question or sub-aspect, never decorative.
Paragraph	Typical chunk unit	3 to 6 lines. One idea per paragraph.
List	Near-extractable as-is	Standalone items, no "see above" references.
Table	Extracts very well	Clear headers, short cells, avoid merged cells.

Standalone passages: test each one

Simple test: copy any paragraph of your page and paste it into an empty message to a colleague. If the paragraph stays understandable, it's standalone.

Avoid pronouns without an antecedent ("it enables..." mid-page).
Re-name the main entities at the start of each section.
Define acronyms at their first local occurrence, not only at page top.
Date time-bound statements ("in 2026", not "this year").

Citation-friendly content

A cited passage is one the model can display with confidence. It has three traits:

A sharp claim — "Google AI Overviews rolled out broadly in 2025" is citable. "AI is changing SEO" isn't.
Minimum context — who, what, when. No ambiguity on the subject.
Verifiability — an external source, a published datum, an author.

Entities and disambiguation

LLMs bind your content to entities. If your brand shares its name with something else (a plant, a person, another company), disambiguation is priority one. Techniques:

Systematic co-occurrence with domain markers: sector, product, customer segment.
Foundational links to Wikipedia, Wikidata, the official LinkedIn, the canonical site, via sameAs on Organization schema.
Factual biography on an About page with dates, places, activities, sources.
Editorial consistency: same tone, same terminology across site and adjacent channels (LinkedIn, press, podcasts).

Anatomy of a GEO page

H1 — primary query, 6 to 12 words, no superlatives.
Lede — 2 to 4 sentences that already answer the question. First sentence standalone.
Dates — publication + last update, visible.
H2 "In brief" — 3 to 5 bullets, each citable as-is.
Body — 5 to 8 H2 sections covering sub-intents.
Table or checklist — at least one dense, extractable element.
Contextual FAQ — 3 to 6 local (not generic) questions.
Outbound linking — 3 to 6 internal contextual links, 1 to 3 external source links.
Author and organisation — schema.org Article + Organization.

Length, format, density

There's no magic length. A page must cover its subject, not hit a word quota. Benchmarks:

Pillar: 2,000 to 4,000 words, 6 to 10 H2s.
Satellite: 800 to 1,500 words, 3 to 5 H2s.
FAQ / definition: 400 to 800 words, standalone answers.

Common mistakes observed

Walls of text — 15-line paragraphs, invisible in retrieval.
Decorative H2s: "Conclusion", "Introduction", "Learn more" — carry zero query.
JSON-LD schemas inconsistent with visible content (missing author, fake date, wrong type).
Unreviewed AI-generated content, stacking empty phrasing.
Cross-page duplications that dilute authority.
Long conditional sentences saying nothing citable.

Express checklist

Each H2 carries a clear intent and reformulates a query.
Each paragraph can be read in isolation.
Every numerical claim is dated and sourced.
Every acronym is defined at first occurrence.
The page contains at least one table or checklist.
The page carries a visible update date.
Internal linking goes out to at least 3 other pages on the site.
schema.org structured data is validated.

Content structure for LLMs