What Is Schema Markup? Definition and Example

Schema markup is structured data, usually written in JSON-LD with the schema.org vocabulary, that tells search engines explicitly what a page describes, such as an organization, product, article or event. It can make pages eligible for rich results in Google (Google Search Central). For AI features, Google says no special schema is required (AI features), but clean entity data still helps engines understand who you are.

What does it look like?

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Example Co",
  "url": "https://example.com",
  "logo": "https://example.com/logo.png",
  "sameAs": [
    "https://www.linkedin.com/company/example",
    "https://x.com/example"
  ]
}

sameAs links your official profiles so they can be tied to one entity (schema.org Organization; Google Organization docs).

Which types matter most for marketers?

What are the common mistakes?

• Marking up content that isn't visible on the page.
• Conflicting names or URLs across pages.
• Expecting schema alone to get you into AI answers.

How do you validate it?

Use Google's Rich Results Test and the Schema Markup Validator, then watch the enhancement reports in Search Console.

Does schema help in ChatGPT or Perplexity?

Those companies don't publish a requirement for schema. What's clear is that the visible text on the page is what gets read and quoted. Treat schema as a way to keep your entity data consistent (name, logo, profiles, product details), not as a shortcut into AI answers. Keep it accurate and in sync with what's on the page.

Related

• Schema markup for AI search
• Generative engine
• Google Search Central, Documentation updates, accessed 30 Oct 2026