> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getcited.in/llms.txt
> Use this file to discover all available pages before exploring further.

# Structured Data

> Machine-readable information embedded in web pages using standards like Schema.org — helps LLMs parse and cite content accurately.

**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.

## Why it matters

LLMs that use retrieval (like [Perplexity](/concepts/platforms/how-perplexity-ranks-sources) and [ChatGPT](/concepts/platforms/how-chatgpt-search-works)) 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 [AEO/GEO](/glossary/geo), structured data is a technical foundation that sits alongside [llms.txt](/glossary/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](/concepts/platforms/how-gemini-search-works) 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](/glossary/schema-markup) — the specific implementation of structured data using Schema.org
* [What sources LLMs cite](/concepts/foundations/ai-citation-sources) — structured data as a citability factor