What is schema markup for AI?
Schema markup for AI means structured data, usually JSON-LD, that describes a page’s entities and facts for machines. It can support ordinary search understanding. It is not a special tag that adds you to ChatGPT or to Google’s AI answers. Google has said its AI search features do not require markup beyond normal Search eligibility.
Why it matters for AI visibility
Structured data helps when it restates visible facts: who the organization is, what the product does, which questions a page answers, and who wrote an article. That supports entity clarity and can make a page eligible for search features that depend on a type of markup. It matters for AI visibility only as part of being a clear, eligible page. It does not select citations. Adding FAQ markup to text the page does not show, or marking up reviews the page does not contain, violates the point of schema and can conflict with search guidelines. Write the answer in HTML first. Mirror it in JSON-LD if a documented type fits. Keep author, organization, and product details consistent with the rest of the site. When an engine fails to cite you, inspect the answer’s sources and your crawl access before you add another schema type.
Example
A guide answers five buyer questions in visible headings and paragraphs, then adds FAQ structured data that matches those pairs word for word. That is honest schema. A homepage that injects FAQ markup for questions the page never asks is not a GEO tactic. It is mismatched markup.
Related terms
Entity SEO is the broader naming and corroboration work schema can support. llms.txt is a separate site-level file, not structured data. E-E-A-T is not a schema type you can add to force trust. Visible expertise still has to appear on the page.