Schema markup is structured data added to a web page that describes its content in a machine-readable format, using a shared vocabulary. It helps search engines and AI systems understand what a page contains, which can improve how it is displayed and how reliably it is interpreted and cited.
Schema markup is unglamorous and it is one of the highest-leverage technical things you can do for AI visibility, because it removes ambiguity about what your pages actually say. Machines interpret structured data far more reliably than they infer meaning from layout. For content meant to be cited by AI systems, it moves you from hoping to be understood to stating it explicitly.
Example:
Marking up a glossary entry as a defined term, and its questions as an FAQ, tells machines exactly what the page contains rather than leaving them to guess from formatting.
What does schema markup do?
It describes a page's content in a machine-readable format, helping search engines and AI systems reliably understand what the page contains.
Why does schema markup matter for AI search?
Structured data removes ambiguity, making content easier for AI systems to interpret accurately and reference in generated answers.