What is schema markup used for?

It labels the meaning of content so machines can understand it without guessing — that a string is a price, a rating, an author, or a question and its answer. That understanding can unlock rich results in search and helps engines and AI reliably extract and reuse the facts on a page.

Which format should I use?

JSON-LD. It is Google's recommended format and the easiest to maintain because the markup sits in a single script block rather than being interleaved through your visible HTML (as Microdata and RDFa are). All three encode the same Schema.org vocabulary; JSON-LD just keeps it cleanly separated from the page.

Does schema markup help with AI search?

Indirectly. Schema does not force an AI engine to cite you, but clearly labeled entities and facts make your content easier to parse and less ambiguous — which helps models pull the right details and attribute them to you rather than misreading the page.

Glossary · How AI reads content

Schema Markup

Structured-data code, using the Schema.org vocabulary, that labels the meaning of your content so search engines and AI can understand and reuse it.

Updated August 20265 min readReviewed by PageLens.ai

JSON-LD · Organization schema

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "YourBrand",
  "url": "https://yourbrand.com",
  "sameAs": ["…/company/yourbrand"]
}

A small block of JSON-LD tells machines this page describes a specific organization.

Schema markup is code you add to a page to label what its content means, using a shared vocabulary that search engines and AI systems already understand. It turns a paragraph a human reads at a glance into data a machine can parse without guessing.

What schema markup actually is

Schema markup uses the Schema.orgvocabulary — a shared standard created in 2011 by Google, Microsoft, Yahoo and Yandex — to describe the entities on a page: an organization, a product, a review, an article, an FAQ. Where a human infers that “$49/mo” is a price, a machine only sees text. Schema markup states it explicitly: this is a Product, this is its price, this is its aggregateRating.

In practice it is almost always written as JSON-LD— a small block of structured JSON dropped into the page’s HTML. Google recommends JSON-LD over the older Microdata and RDFa formats because it keeps the markup in one place instead of scattering attributes through your visible content.

Schema markup vs. structured data

The two terms are often used interchangeably, but they name different things. Structured data is the general concept — any machine-readable labeling of a page. Schema markup is the specific vocabulary and implementation most sites use to do it.

Structured data

Schema markup

The broad concept: machine-readable labels

One specific way to add them

Format-agnostic idea

Uses the Schema.org vocabulary

Can use various standards

Usually written in JSON-LD

What it enables

Schema markup does two useful jobs at once, both of which matter more as search becomes answer-led:

Rich results. Correctly marked-up pages can qualify for enhanced search listings — star ratings, FAQ dropdowns, product prices, breadcrumbs — that stand out and earn more attention.
Machine understanding. It tells search engines and AI what each element is, which helps them connect your brand, products and people to the right entities rather than treating your page as an undifferentiated blob of text.

Getting it right

The markup has to describe content that is genuinely on the page — marking up a rating you don’t display, or an FAQ that isn’t visible, breaks the guidelines and can cost you the rich result entirely. Start with the entity that matters most (usually your Organization), keep the values accurate and in sync with the visible page, and validate before shipping. Done well, schema is quiet infrastructure: it makes everything else you publish easier for machines to read and reuse.

Frequently asked

What is schema markup used for?: It labels the meaning of content so machines can understand it without guessing — that a string is a price, a rating, an author, or a question and its answer. That understanding can unlock rich results in search and helps engines and AI reliably extract and reuse the facts on a page.
Which format should I use?: JSON-LD. It is Google's recommended format and the easiest to maintain because the markup sits in a single script block rather than being interleaved through your visible HTML (as Microdata and RDFa are). All three encode the same Schema.org vocabulary; JSON-LD just keeps it cleanly separated from the page.
Does schema markup help with AI search?: Indirectly. Schema does not force an AI engine to cite you, but clearly labeled entities and facts make your content easier to parse and less ambiguous — which helps models pull the right details and attribute them to you rather than misreading the page.