Structured Data & Schema for AI
Sveip for å vise menyen
Schema markup is a form of semantic vocabulary—or code—added to a website's HTML to help search engines like Google, Bing, and Yahoo better understand the context of its content.
For traditional SEO, schema markup has long been a best practice for earning rich results in Google. For GEO, it performs a different but equally valuable function: it reduces the ambiguity a language model has to resolve when interpreting your page. An AI engine reading an unmarked page has to infer context from the prose. An AI engine reading a well-marked page gets that context served directly — which makes parsing faster, interpretation more accurate, and the content more reliably synthesizable.
Interactive Schema Type Explorer
Key fields:
- headline — full title of the article — match the H1 exactly;
- author.name — Named author entity — links to Person schema;
- author.url — URL to author bio or professional profile;
- datePublished: ISO 8601 date — initial publication date;
- dateModified: last updated date — critical freshness signal
- publisher.name: organisation name — links to Organization schema;
- description: 150-160 char summary — used in knowledge graph extracts.
Key fields:
- mainEntity: array of Question objects;
- Question.name: the question text — should match real user query phrasing;
- Question.acceptedAnswer: the answer entity object;
- acceptedAnswer.text: the answer text — keep under 300 words per answer.
Key fields:
- name: the goal the how-to achieves;
- step: array of HowToStep objects in sequence;
- HowToStep.name: short step title — verb + object
- HowToStep.text: full step instructions;
- totalTime: ISO 8601 duration — optional but useful;
- tool / supply: prerequisites listed as Tool or HowToSupply objects.
Key fields:
- name: full name — consistent across all bylines;
- url: canonical author page URL on your domain;
- sameAs: LinkedIn, institutional profile, ORCID, or similar;
- jobTitle: Professional role — signals subject relevance;
- knowsAbout: array of topics the author has expertise in;
- alumniOf: educational or institutional credentials.
Key fields:
- name: legal or trading name — consistent across all pages;
- url: canonical homepage URL;
- logo: ImageObject with URL and dimensions;
- sameAs: array of authoritative profile URLs (LinkedIn, Wikipedia, Crunchbase, etc.);
- description: concise organisation description;
- foundingDate: year founded — entity credibility signal.
Key fields:
- name: product name — exact match to product title
- description: feature-focused description;
- aggregateRating: ratingValue and reviewCount — from real reviews;
- offers: pricing information and availability;
- applicationCategory: for SoftwareApplication — product category;
- operatingSystem: for SoftwareApplication — platform compatibility.
Tables and Lists as Native Structured Content
Schema markup is the formal structured data layer. But there is a simpler, complementary form of structured content that AI engines parse with high reliability: HTML tables and well-formatted lists. These are the on-page equivalents of schema — they impose structure on information that prose cannot convey efficiently.
Comparison tables are particularly effective for GEO citation in competitive analysis content. When an AI engine synthesizes an answer to "what's the difference between X and Y," a well-structured HTML table with clear column headers and consistent row data gives the engine extraction-ready material. The table survives synthesis intact in a way that the same information in paragraph form does not.
Any comparison, ranking, or specification content that exists in your prose should also exist as an HTML table or structured list. The prose is for human reading; the table is for machine extraction. Both serve a purpose. Neither replaces the other.
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