The schema types that matter most for AI

Schema.org structured data, usually delivered as JSON-LD, is a layer of explicit labels on top of your visible content. It tells a machine 'this is the author', 'this is the published date', 'this block is a question and this is its answer.' Engines can infer some of this from raw HTML, but inference is error-prone; schema removes the ambiguity.

For GEO, that clarity matters because extraction and attribution are the whole game. The easier you make it for an engine to identify your entity, your claims, and your answer blocks, the more confidently it can cite you. You don't need every schema type - you need the few that describe your most citable content.

These cover the majority of GEO value for most sites.

Use JSON-LD in the page head - it's the format engines parse most reliably and it keeps structured data separate from your markup. Start with Organization sitewide, then add the page-level type that fits each page's job: Article for guides, FAQPage where you have real FAQs, HowTo for genuine instructions.

Keep the schema accurate and complete enough to be useful, but don't invent structure that isn't on the page. The single most important rule: the structured data must describe what a human actually sees. Marking up FAQs that don't appear, or claiming an author who didn't write it, is a spam signal - and it's the fastest way to lose trust.

Structured data makes good content easier to parse; it does not make weak content citable. An FAQPage schema wrapped around vague, padded answers won't earn citations - the engine can extract the block, but the block has nothing worth quoting. Schema amplifies clarity that's already there.

Think of it as the bottom layer of a stack: answer-first writing and real evidence supply the substance, schema labels it so machines can find it, and an llms.txt surface helps crawlers discover it. Each layer matters, but schema's job is specifically to remove parsing ambiguity, not to manufacture quality.