What Is Structured Data? Machine-Readable Markup for Search and AI
Structured data is markup embedded in a web page that describes its content in a machine-readable vocabulary — declaring "this is a Product, its price is $69, its rating is 4.7" rather than leaving software to infer meaning from prose. In practice the vocabulary is almost always schema.org, and the goal is removing ambiguity for the crawlers, indexes, and language models that consume your pages.
Which formats carry structured data?
Three syntaxes can express the same vocabulary:
| Format | How it embeds | Status |
|---|---|---|
| JSON-LD | A <script type="application/ld+json"> block, separate from visible HTML | Recommended by Google |
| Microdata | Attributes (itemscope, itemprop) woven into HTML tags | Legacy, still parsed |
| RDFa | HTML attributes from the RDF world | Rare outside academic/gov |
JSON-LD won because it decouples annotation from presentation — you can generate, validate, and update it without touching template markup, which is why virtually all modern implementations and Google's structured data documentation center on it.
How do search and AI engines consume it?
Classic search uses structured data for rich results (stars, prices, FAQs in SERPs) and to populate the Knowledge Graph. The AI-era consumption paths are broader: retrieval-augmented engines inherit entity understanding from the underlying indexes; grounding pipelines that fetch your page receive the JSON-LD as parseable text inside the HTML; and entity resolution — deciding that "Menra" the string refers to a specific software company — leans on Organization markup with sameAs links to authoritative profiles. Structured data will not make weak content citable, but it disambiguates who is saying what, which compounds across every page.
What does a sensible priority order look like?
Start with identity, then content types. Sitewide Organization markup establishes the brand entity; Article with real author and date fields supports credibility assessment; Product with offers powers shopping-flavored answers; FAQPage mirrors the question-answer structure engines extract from; BreadcrumbList encodes hierarchy. Validate everything with the Rich Results Test and the schema.org validator, and keep markup truthful — values must match visible content. Teams doing AEO-focused content work treat structured data as the machine-facing half of every page shipped, not a post-launch garnish.
Frequently asked questions
- Do LLMs actually read structured data?
- Indirectly and increasingly directly. Retrieval-based engines inherit it through the search indexes they ground on — Google and Bing parse it deeply — and structured data feeds the knowledge graphs engines use for entity resolution. JSON-LD in fetched HTML is also plain text an LLM can parse.
- Which structured data types matter most for AI visibility?
- Organization (brand identity and sameAs links), Article (authorship and dates), Product (offers and ratings), FAQPage (explicit Q&A pairs), and BreadcrumbList (site hierarchy). These map to how engines resolve entities, assess trust, and extract answers.
Keep exploring
See how AI engines talk about your brand — track mentions across ChatGPT, Perplexity, Claude, Gemini and 5 more. Start with Menra