Which schema type should I use for my page?

Use Article for editorial content, HowTo for step-by-step guides, FAQPage for Q&A content, and Product for product pages. You can combine multiple types — for example, an Article with an embedded FAQPage block.

Does schema markup directly affect my Google rankings?

Schema markup doesn't directly boost rankings, but it enables rich results (which increase CTR) and provides structured signals that AI Overviews and AI engines use when selecting citation sources. The indirect effect on visibility is significant.

How do I validate my schema markup?

Use Google's Rich Results Test (search.google.com/test/rich-results) or Schema.org's validator. Both will highlight missing required fields and syntax errors. Run validation every time you modify the markup.

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Core GEO Signals · Published Mar 31, 2026

Schema Markup for AI Engines

JSON-LD structured data that tells AI crawlers what type of content a page contains.

TL;DR — JSON-LD structured data gives AI engines a structured fact to cite directly, bypassing prose parsing. The Princeton GEO study (2024) found pages with structured data were cited 30–40% more frequently in AI-generated answers.

Why Schema Markup Matters for AI Engines

AI engines use structured data to bypass prose parsing entirely. When a page includes an Article schema with headline, author, and datePublished, models have a structured fact to cite directly — no interpretation required. This is fundamentally different from how a human reader processes content.

The Princeton GEO study (2024) found pages with structured data were cited 30–40% more frequently in AI-generated answers compared to equivalent pages without markup. The mechanism is straightforward: structured data reduces the model's uncertainty about what a page claims to be.

Schema also enables rich results in Google Search, which AI Overviews inherit. A page with valid Article or FAQPage schema has two compounding advantages: higher citation rates in direct AI responses AND preferential treatment in the Google AI Overview system that feeds LLM training data.

The @type you choose matters. Article works for editorial content. HowTo signals step-by-step instructions. FAQPage signals a question-answer format. Product signals commercial intent. Choosing the wrong type is like mislabelling a file — the AI may find it, but it won't trust it.

How to Implement

Add <script type="application/ld+json"> to <head> with the appropriate @type
Minimum fields for Article: @context, @type, headline, author (as Person), datePublished, publisher
Validate with Google's Rich Results Test

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "How to optimise content for AI engines",
  "author": { "@type": "Person", "name": "Jane Smith" },
  "datePublished": "2025-01-15",
  "publisher": { "@type": "Organization", "name": "TrustData" }
}

For FAQ content, add a FAQPage block in addition to the Article block — they can coexist on the same page.

Common Mistakes

Using Microdata instead of JSON-LD — Microdata is harder for crawlers to extract and is no longer recommended by Google
Missing author or using a generic org name — "author": { "@type": "Organization" } is not equivalent to a named person; AI engines treat it as lower-trust content
Not updating datePublished/dateModified — stale dates signal stale content to both search engines and AI crawlers

Sources

Frequently Asked Questions

Related Signals

FAQ Blocks for AI Citability

How FAQ schema makes your Q&A content directly extractable by AI engines.

Author Attribution

Why named authors in schema increase content trust signals.

Content Freshness

How datePublished and dateModified affect AI engine citation rates.

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