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GEO

Content Structure for AI Citability

How to structure your articles and guides so AI engines can accurately extract, summarize, and cite your content in generated responses.

The Extractability Problem

AI engines do not read pages the way humans do. They extract passages — chunks of text ranging from a sentence to several paragraphs — that are relevant to the query being answered. Whether your content is extracted and cited depends heavily on how it is structured. Well-structured content is extractable content.

Three factors determine extractability: semantic clarity (does the HTML structure signal what the content is?), proximity (is the answer close to the question/heading that asks it?), and self-containment (does the passage make sense on its own, without requiring the surrounding context?).

The Inverted Pyramid: Lead with the Answer

Journalists use the "inverted pyramid" structure: the most important information first, supporting details second, background last. AI citation engines use a similar heuristic. When a retrieval system sees a heading like "What is WCAG AA compliance?" it looks for the answer in the first 1–3 sentences that follow. If those sentences contain the answer, the passage will be extracted. If they contain a preamble about why the topic is important, the system may move on.

Apply this at every heading level. Each h2 section should answer the implied question of its heading in the first sentence or two. Each paragraph should begin with its main point.

<!-- Bad: buries the answer -->
## What is WCAG AA compliance?

Web accessibility has been an increasingly important topic in recent years.
Many countries have passed laws requiring websites to meet certain standards.
There are multiple levels of conformance that organizations can aim for.
WCAG Level AA is one of these levels...

<!-- Good: leads with the answer -->
## What is WCAG AA compliance?

WCAG Level AA compliance means a website satisfies all 50 success criteria
at Levels A and AA of the Web Content Accessibility Guidelines 2.1 or 2.2.
This is the legal standard required by the EU Web Accessibility Directive,
Section 508, and most national accessibility regulations.

Heading Density and Granularity

Use more headings, not fewer. Each heading is an anchor point for AI extraction. A 1500-word article with only an h1 and three h2s forces the extraction engine to guess which paragraphs belong to which topic. The same article with six h2s and relevant h3 subheadings provides 9+ extraction anchors, each one targeting a specific sub-query.

  • Use h2 for major sections; use h3 for sub-topics within a section.
  • Write headings as implied questions or direct answers (e.g., "How to Test Keyboard Navigation" or "Keyboard Navigation Testing: Step by Step").
  • Keep h2 sections between 150–400 words — short enough to be a coherent extraction unit, long enough to be substantive.

Short Paragraphs and Single-Topic Sentences

Long paragraphs are extraction nightmares. If a 200-word paragraph covers three different points, an AI extracting 50 words of it will likely get an incomplete or misleading answer. Write short paragraphs of 2–4 sentences, each focused on one idea. This mirrors accessibility best practices for cognitive readability (plain language, chunked content) and is simultaneously better for AI extraction.

Definition Patterns for Maximum Extractability

AI engines are particularly good at extracting definitional content. Pages that define key terms are frequently cited when users ask "what is X?" questions. Use consistent definition patterns:

  • "X is..." — direct definition sentence
  • "X refers to..." — explanatory definition
  • "The term X means..." — formal definition
  • A glossary section at the bottom of a guide with term: definition pairs is extremely extractable.
WCAG (Web Content Accessibility Guidelines) is a set of internationally recognized technical standards published by the W3C that define how to make digital content accessible to people with disabilities. The current version, WCAG 2.2, was published in October 2023 and contains 87 success criteria organized into four principles: Perceivable, Operable, Understandable, and Robust.

Tables and Lists as Structured Fact Repositories

Tables and lists are ideal containers for factual information that AI systems can extract verbatim. A table comparing WCAG versions, a list of countries with digital accessibility laws, or a numbered procedure for running an accessibility audit are highly extractable because they present information in a form that is both human-readable and machine-parseable.

Ensure all tables have a <caption> or preceding heading that identifies what the table contains. Ensure lists are preceded by a sentence that introduces their contents. This context helps AI engines attribute the extracted list correctly.

Content Freshness Signals

AI systems weight recent content higher for queries where recency matters. Signal freshness explicitly:

  • Add a "Last updated" date near the top of the article.
  • Use dateModified in your Article schema.
  • Include version-specific information where relevant (e.g., "As of WCAG 2.2, published October 2023...").
  • Periodically review and update guides to reflect new standards, laws, and tools.

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