Schema Markup Guide: 2026 Best Practices
A 2026 schema markup guide: FAQPage, Article, Organization schema, and structured data from a GEO perspective.
What Is Schema Markup?
Schema markup is a standardized vocabulary that explicitly tells machines what the content on a web page means. It is built on the Schema.org vocabulary and is a structured-data format jointly supported by search engines like Google, Bing and Yandex.
Structured data describes not just how a page looks but what it is about. Marking a price as a price, a date as a publish date, or a person as the author removes the ambiguity systems face when interpreting content.
Why JSON-LD?
Schema markup can be implemented in three formats: JSON-LD, Microdata and RDFa. Among these, Google explicitly recommends JSON-LD. The reason is that JSON-LD separates structured data from the visible HTML; the data is added as a single script block in the page head or body.
JSON-LD - Google's recommended format; added as a script block independent of HTML, easy to maintain.
Microdata - Tags are embedded directly into HTML elements; harder to maintain and scatters the markup.
RDFa - An older format that extends HTML attributes; rarely preferred in modern implementations.
The Most Important Schema Types
Article
Article schema is used for blog posts and news content. The headline, author, datePublished, dateModified and publisher fields let both search engines and AI systems understand the author, freshness and publisher of the content.
FAQPage
FAQPage schema marks up question-and-answer content. Each question is defined as a Question and each answer as an Answer. This structure is especially valuable for GEO because it provides clear question-answer pairs that AI systems can cite directly.
Organization
Organization schema defines your brand as an entity. The name, logo, url, sameAs and contactPoint fields help your brand be represented correctly in the knowledge graph and let AI systems position you as a recognizable authority.
Schema from a GEO Perspective
Structured data helps generative AI systems parse content without ambiguity. An LLM cites a clear question-answer pair defined with FAQPage markup more confidently than untagged free text. By making content machine-readable, schema raises the likelihood of being cited.
Validation and Shipping
Before shipping schema, check it with the Google Rich Results Test and the Schema.org validator. Broken or incomplete markup can prevent rich results from appearing. After shipping, monitor the count of valid markup and any errors via the Enhancements reports in Search Console.