How to structure a page so AI can actually understand it

When an AI model reads your page, it doesn't experience it the way a human does. It processes the underlying HTML, extracting meaning from structure, hierarchy and text density. Pages optimised for human skimming are often opaque to AI systems — and that opacity translates directly into lower citation rates.

The foundation is clean semantic HTML. Use h1 for your primary topic, h2s for main subtopics, h3s for supporting detail. Don't nest headings for visual effect — use them to signal information architecture. AI models treat heading structure as an outline of your content. If the outline is clear, the content is citable.

Next, write for extractability. Each section of your page should be able to stand alone as a meaningful answer. Write clear topic sentences. Avoid burying key claims in long paragraphs. Use lists where appropriate — AI systems are good at parsing lists and often reproduce them directly in answers.

Include a concise description of what your page covers near the top — ideally in the first 150 words. This acts as a summary that AI models can pull when deciding whether to cite you. Think of it as writing a brief abstract for each key page, not just for the homepage.

Finally, avoid heavy JavaScript rendering for content that matters. If your key claims are rendered client-side, many crawlers — including AI bots — will never see them. Server-render your core content and ensure it's present in the HTML source, not injected after page load.