Schema markup
Schema markup is in-page code (usually JSON-LD) that labels content with semantic types and properties so machines — including AI engines — can extract structured facts without parsing prose.
Schema markup is the practical implementation of the Schema.org vocabulary. It is the most direct way to tell an AI engine: this paragraph is a FAQ answer, this number is a price, this person is the author, this date is when the article was last updated.
Most useful schemas for GEO
| Schema | Use case |
|---|---|
| Article | Authored content, news, blog posts |
| FAQPage | Question-and-answer pages |
| HowTo | Step-by-step instructions |
| Product | Pricing, features, ratings, availability |
| Organization | Brand identity, logo, founders, social profiles |
| Person | Author bio, credentials |
| Review | Star ratings and review bodies |
| BreadcrumbList | Site hierarchy |
| Service | Service offerings with descriptions |
| SoftwareApplication | SaaS products, mobile apps |
Why it matters for AI search
When a model extracts a passage from your page, schema markup increases confidence that the passage means what it appears to mean. A page with FAQPage schema gets quoted as an authoritative FAQ answer; the same text without schema may be paraphrased less faithfully or skipped.
Implementation note
Use JSON-LD (structured data embedded in a script tag), validate with the schema markup validator, and keep the markup in sync with the on-page facts. Drift between schema and visible content is a strong negative signal.
