Using Structured Data (Schema/JSON-LD) to Increase AI Citations
Why Schema helps AI
Humans infer from context that "this is the author, this is the price, this is a FAQ." Machines do not. Declaring entities in JSON-LD lets AI extract facts without misreading them — especially valuable for the fact-grounded citations in generative answers.
Types to apply first
- Organization / Person: identity of brand and author — an E-E-A-T trust signal
- Article / BlogPosting: title, author, published/updated dates
- FAQPage: question-answer — directly useful for AEO/GEO
- BreadcrumbList: site structure
- Product / Review: essential for commerce
Example (FAQ)
<script type="application/ld+json">
{
"@context":"https://schema.org",
"@type":"FAQPage",
"mainEntity":[{
"@type":"Question",
"name":"What is GEO?",
"acceptedAnswer":{"@type":"Answer",
"text":"Optimizing so generative AI cites your site in its answers."}
}]
}
</script>Cautions
Schema must match what is actually visible on the page. Marking up hidden information is treated as spam. After applying, validate with a rich-results test. To see which types you lack, check the technical items in a free audit.
Frequently Asked Questions
Does structured data help with AI citation?
It states the meaning of a page in a machine-readable form, which helps AI extract facts such as author, dates and question-answer pairs without misreading them.
Which schema types should I add first?
Start with Organization and Article to declare identity and publish/update dates, then FAQPage and BreadcrumbList to express question-answer pairs and site structure.
JSON-LD or microdata?
JSON-LD. It is kept separate from the body markup, which makes it easier to maintain, and it is the format major search and AI engines officially recommend.
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