Schema Markup
Structured data added to a web page using a shared vocabulary (schema.org) that explicitly tells search engines and AI systems what the content means.
Schema markup is structured data, usually in JSON-LD, that labels the meaning of content on a page using the schema.org vocabulary. It lets you state facts explicitly — that a block is an FAQ, a product, an organization, or a defined term — rather than leaving machines to infer them.
Why it matters for AI
Both search engines and AI systems parse structured data to understand entities and facts with high confidence. Clear schema reduces ambiguity about who you are and what you offer, making your content easier to extract and cite accurately.
Useful types for an AEO strategy
Organization/LocalBusiness— core identity and contact factsFAQPage— question/answer pairsDefinedTerm— glossary entriesProduct,Service,Article
Practical takeaway
Schema won’t manufacture authority, but it removes ambiguity — and ambiguity is a common cause of inaccurate AI answers about a business.
Related Terms
- Optimization Tactics
Content Chunking
Structuring content into self-contained, clearly-labeled sections so AI systems can extract and cite a specific passage without needing the whole page.
- Optimization Tactics
GPTBot
OpenAI's web crawler, which gathers content used to train and inform its models; site owners can allow or block it via robots.txt.
- Optimization Tactics
llms.txt
A proposed standard file placed at a site's root that offers AI systems a curated, plain-text map of the site's most important content.