Schema Markup for AI Citations 2026: How to Get Cited by ChatGPT & Co.

What is schema markup for AI search citations?

Schema markup for AI search citations is structured data in JSON-LD format, a standardized vocabulary (schema.org) used to describe content in a machine-readable way - usually embedded invisibly in the <head> section. It helps AI systems like ChatGPT, Perplexity, and Google AI Overviews understand a page's content correctly and cite it as a source, instead of an AI having to guess who wrote an article or what a product costs from body text.

For classic SEO, schema markup enables rich results - star ratings, FAQ boxes, pricing shown directly in search results. For GEO the effect is similar, but even more direct: AI models use structured data to extract facts precisely instead of interpreting them out of ambiguous text. Along with llms.txt it's one of the two central technical foundations of GEO.

Implement in this order

The order isn't arbitrary - each type builds on the trust and context signal of the one before it.

Organization schema, ready to copy

The minimum every website should start with - ready to use as-is:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Your Company",
  "url": "https://your-domain.com",
  "logo": "https://your-domain.com/logo.png",
  "sameAs": ["https://linkedin.com/company/..."]
}
</script>

Next, FAQPage schema - but only for questions that are also visible on the page (more on that below):

{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [{
    "@type": "Question",
    "name": "Your question, word-for-word matching the visible text",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "Your answer, word-for-word matching the visible text"
    }
  }]
}

For blog posts, add Article schema too - the exact type this article itself carries in its <head>:

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Your article's title",
  "datePublished": "2026-01-15T09:00:00+02:00",
  "dateModified": "2026-09-12T09:00:00+02:00",
  "author": { "@type": "Person", "name": "Author name" },
  "publisher": {
    "@type": "Organization",
    "name": "Your Company",
    "logo": { "@type": "ImageObject", "url": "https://your-domain.com/logo.png" }
  }
}

For the priority table above, this page also carries HowTo schema in its <head> - invisible in the text, but machine-readable for the implementation order itself.

Free generators

If writing code by hand isn't an option, these three produce valid JSON-LD from a form:

How to test schema markup for free

Google's Rich Results Test is the standard check: enter a URL and every schema type found is displayed along with errors and warnings. For pure syntax validation without Google's rich-result-specific scoring, the Schema Markup Validator from schema.org itself is a good addition.

The most common mistake: schema that doesn't match the visible content

One mistake we keep seeing in audits, even with technically sharp teams: the FAQ schema in JSON-LD lists different or additional questions than what's actually visible on the page. It happens easily when FAQ content and schema are maintained in two separate places in the code, and a content change only gets applied to one of them.

The problem: both Google and AI models cross-check structured data against the visible HTML. A mismatch doesn't just mean the extra schema questions have no effect - Google explicitly treats blatant discrepancies between markup and visible content as a policy violation.

Frequently asked questions about schema markup