Schema Markup (Structured Data)
Schema markup is structured data markup, usually in JSON-LD format, that describes a page's content in a language search engines and AIs understand unambiguously. It helps Google and LLMs interpret and cite your content correctly.
Schema markup is structured data code — most often in JSON-LD — that explicitly describes what a page contains: article, FAQ, organisation, author, breadcrumb. It translates your content into a language Google and LLMs read without guessing, which reduces interpretation errors and encourages citation.
How schema markup works
You add standardised annotations (schema.org) to the page's code that say "this is a question", "this is the author", "this is the update date". Search engines use them to generate rich results, and RAG systems use them to isolate the right passages. The most useful types: Article, FAQPage, Organization, Person, BreadcrumbList.
A concrete example
A service page with a well-implemented FAQPage schema can display its questions directly in Google's results, and provides an AI Overview with ready-to-cite question/answer pairs. It's one of the easiest gaps to close against competitors who have no schema at all.
Why it matters
Many agencies neglect schema: that's precisely where there's an advantage to seize. It serves the AI Overview, the featured snippet and E-E-A-T. To deploy it properly, see our SEO agency.
Schema means speaking to machines in their own language: less ambiguity, more citations.
Frequently asked questions
Indirectly: it doesn't boost rank on its own, but it makes your page eligible for rich results and helps AIs interpret your content. This machine clarity increases your chances of appearing in snippets and AI answers.
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