Structured data is machine-readable information embedded in web pages that helps search engines and LLMs understand content type, meaning, and relationships. The most widely used standard is Schema.org, implemented via JSON-LD (the recommended format), microdata, or RDFa.Documentation Index
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Why it matters
LLMs that use retrieval (like Perplexity and ChatGPT) parse retrieved pages before synthesizing answers. Structured data makes this parsing more reliable — a page with Article schema, clear author attribution, and datePublished metadata is easier for an LLM to categorize and cite than unstructured HTML. For brands pursuing GEO, structured data is a technical foundation that sits alongside llms.txt and crawler permissions.How it applies in practice
Common structured data types relevant to AI visibility include Product (e-commerce), Article (blog posts), FAQPage (Q&A content), HowTo (guides), and DefinedTerm (glossary entries). Each type helps LLMs understand what the page is about and how to reference it. Gemini tends to weight structured data more heavily than other platforms because it benefits from Google’s deep Schema.org processing infrastructure.Related concepts
- Schema markup — the specific implementation of structured data using Schema.org
- Schema markup and platform tradeoffs — practical limitations
- What sources LLMs cite — structured data as a citability factor