Structured Data for AI: Schema Markup That LLMs Actually Use

Structured data is machine-readable information embedded in your web pages that explicitly describes content, entities, and relationships. For AI visibility, structured data serves as a translation layer between human-readable content and AI model comprehension.

What Is Structured Data?

Structured data uses Schema.org vocabularies to annotate web content with explicit metadata. The most common format is JSON-LD embedded in HTML script tags.

Which Schema Types Matter for AI Visibility?

Organization schema defines your brand identity. Product schema describes offerings with specific attributes. Article schema identifies authorship and publication details. FAQPage schema marks up question-answer pairs. HowTo schema structures step-by-step instructions. Review and AggregateRating schemas provide social proof data.

How AI Models Use Structured Data

Retrieval-augmented models parse structured data during real-time retrieval. Training-data models benefit indirectly. Structured data also reduces hallucination risk by providing explicit, verifiable information.

Implementation Best Practices

Start with Organization schema on your homepage. Add Product schema to product pages. Implement FAQPage schema on Q&A content. Use Article schema on blog posts. Validate using Google's Rich Results Test. Citerna's audit includes structured data validation.

Common Mistakes

Over-marking content with irrelevant schema types dilutes the signal. Inconsistent entity information confuses AI models. Outdated structured data leads to inaccurate AI citations.