Technical Standards

Structured Data (Schema.org)

Structured data is machine-readable markup, most commonly in JSON-LD format using the schema.org vocabulary, that labels the meaning of content on a page (Organization, Product, FAQPage, Article, Breadcrumb) so search engines and AI systems can parse it without having to guess from raw HTML.

Also known as:schema markup, schema.org, JSON-LD, rich data

HTML expresses layout and visual structure, but it does not say what a page is about. Structured data adds an explicit semantic layer. With a JSON-LD block in the page head, a site can tell a parser, "this is an Organization with this name and these social profiles", or "this section is a list of FAQ entries with these questions and answers". The schema.org vocabulary defines the standard types and properties.

For AI engines, structured data does three useful things. It removes ambiguity about what the page is. It exposes a clean, predictable shape that retrieval and citation systems can lift directly. And it strengthens entity-level signals by tying the page to a known entity record. FAQPage, HowTo, Article, Organization, Product, BreadcrumbList and DefinedTerm are among the most commonly useful types for AI visibility work.

Structured data is not a ranking boost on its own; it is a comprehension aid. The largest gains come when the markup honestly reflects what the page actually contains and when the rest of the page is already clear, accurate and well-scoped. Bad or misleading markup is worse than no markup, because it can be detected and downranked.

Key points

Machine-readable markup, usually in JSON-LD using schema.org types.
Tells parsers what a page is, not just how it looks.
Helps AI engines parse, retrieve and cite content cleanly.
Must accurately reflect the page contents to be useful.

Frequently asked questions

What is schema markup?

Schema markup is structured data that uses the schema.org vocabulary to label the meaning of content on a page (organization, product, FAQ, article, breadcrumb), usually expressed as a JSON-LD block in the page head.

Does schema markup help AI search?

Yes. It makes pages easier for AI engines to parse, retrieve from and cite. It is especially helpful for FAQ, HowTo, Article and Organization content. Markup that is inaccurate or stuffed can hurt rather than help.

Which schema types matter most for AI visibility?

The most commonly useful types for AI visibility are Organization, Product, FAQPage, HowTo, Article, BreadcrumbList and DefinedTerm. Pick the types that genuinely describe the page rather than adding markup for its own sake.

Related terms

Knowledge Graph

A knowledge graph is a structured database that represents real-world entities (people, companies, products, places) as nodes and the relationships between them as edges, used by search engines and AI systems to reason about who is who and what is connected to what.

Entity / Entity Authority

An entity is a distinct, identifiable thing in the world (a company, product, person, place or concept), and entity authority is the strength of the signals that connect a brand to its entity record and to the topics it should be associated with in search and AI systems.

Answer Engine Optimization (AEO)

Answer Engine Optimization (AEO) is the practice of structuring content so that answer engines, including AI chatbots and search features that return a direct response, pick a brand or its content as the answer rather than just one of many links.

llms.txt

llms.txt is a proposed plain-text file placed at the root of a website that gives large language models a concise, curated map of the sites most important pages and content sections, so AI systems can find the right pages without having to crawl the entire site.

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