$ man geo/content-extractability

AI engines do not read your content the way humans do. They parse it - breaking it into chunks based on structural signals like headings, paragraphs, lists, and semantic markup. A well-structured 800-word page with clear headings, short paragraphs, and front-loaded answers is more extractable than a 3,000-word wall of text with no structure. Length does not improve extractability; structure does. When an AI engine retrieves your page, it needs to quickly identify which section answers the query, extract the relevant content, and determine whether it is trustworthy enough to cite. Every structural element helps with that process. Headings tell the AI what each section is about. Short paragraphs isolate individual claims. Lists enumerate options or steps. Tables compare data points. Without these structural signals, the AI engine has to do more work to find the answer, and it will often choose a competitor whose page makes extraction easier.

PATTERN

Run this checklist against every page you want AI engines to cite. Does every section start with a descriptive H2 or H3 heading? Does the first sentence after each heading directly address what the heading promises? Are paragraphs under 100 words each? Are lists used for any content that has three or more parallel items? Are comparison or data points presented in tables rather than prose? Does the page have a logical heading hierarchy - H1 for the title, H2 for main sections, H3 for subsections - with no skipped levels? Is there a clear introductory paragraph that summarizes what the page covers? Does the page avoid burying key claims inside long paragraphs? Every item on this checklist makes a meaningful difference in how easily an AI engine can parse and extract from your content. Pages that score high on all items consistently outperform pages that score high on some but fail on others.

CODE

The HTML elements you use matter. AI engines parse semantic HTML more reliably than div-soup with CSS classes. Use article tags to wrap your main content - this tells parsers where the real content lives versus navigation and sidebars. Use section tags to group related content blocks. Use h2 through h4 tags in proper hierarchy - never skip from h2 to h4. Use p tags for paragraphs rather than divs with text. Use ol and ul for lists rather than styled divs that look like lists. Use table, thead, tbody, th, and td for tabular data. Use blockquote for quotes you want attributed. Use strong or b for key terms and claims you want emphasized. Use time tags with datetime attributes for dates. These elements create a clear document structure that any parser can navigate. The ShawnOS.ai wiki system renders each section as a semantic article element with proper heading hierarchy, making every section independently extractable.

PRO TIP

The single most impactful formatting rule for extractability is one claim per paragraph. When a paragraph contains multiple claims, the AI engine has to decide which claim to extract or whether to cite the entire paragraph. Paragraphs with a single, clear claim are extracted cleanly and cited accurately. Here is the difference. Bad: CRM adoption is growing rapidly, with many companies investing in automation. The average company uses 12 different SaaS tools, and integration costs have become a major concern. Meanwhile, AI-powered CRMs are gaining market share. That paragraph has three separate claims, and an AI engine quoting any one of them would misrepresent the paragraph. Good: AI-powered CRMs captured 23 percent of the market in 2025, up from 8 percent in 2023. Clean, specific, one claim. AI engines can quote it directly and accurately. Train your content team to think in single-claim paragraphs and your citation rate will increase measurably within 60 days.