Definition
Schema markup is a standardized vocabulary of tags (from Schema.org) applied to web pages — typically via JSON-LD — that communicates the semantic meaning of content to machines. Rather than leaving search engines and AI systems to infer what your content means, schema markup explicitly declares it: "This is an Article", "This is a FAQ", "This is a Product".
Common schema types for AI readiness include `Article`, `FAQPage`, `Organization`, `BreadcrumbList`, `SoftwareApplication`, `DefinedTerm`, and `HowTo`. Each type tells AI systems exactly what kind of content they're reading and how to parse it.
Why It Matters
Schema markup directly improves AI citation accuracy. When an AI system reads structured data, it can confidently extract facts, dates, authors, and relationships — making your content far more likely to be cited correctly in AI-generated answers.
Examples
- FAQPage schema for question-and-answer pages
- Article schema for blog posts and knowledge base articles
- Organization schema with sameAs links for brand entity building
- BreadcrumbList schema for navigation hierarchy
- DefinedTerm schema for glossary entries