FAQPage schema
A specific schema.org type that declares a page contains a list of question-and-answer pairs, optimized for structured ingestion by search and AI systems.
FAQPage is a schema.org type introduced in 2014 and heavily used since Google's 2019 rich-result rollout. The structure is straightforward: a page-level `FAQPage` object contains a `mainEntity` array of `Question` objects, each with an `acceptedAnswer` of type `Answer`.
FAQPage schema is the single highest-hit-rate structured data type for AI citation across every major answer engine in our data. The explanation is simple: it pre-structures content into exactly the query-answer shape that LLM training and retrieval both favor.
Best practices: the content declared in the schema must also appear visibly on the page (models and Google both penalize hidden schema content), answers should be 40–120 words, and the questions should match real user phrasing rather than marketing-flavored questions.
In AIRRNK
AIRRNK flags every page that has a buyer-intent question block but no FAQPage schema, suggests exact markup, and can inject the schema via the WordPress or Shopify integrations.
- Schema Markup
Structured data embedded in a page (usually as JSON-LD) that describes what the page is about in a machine-readable vocabulary defined at schema.org.
- AI Score
AIRRNK's 0–100 grade for how likely a site is to be cited by a language model, calculated from 47 weighted checks across four pillars.
- Generative Engine Optimization
The practice of making a website more likely to be cited by AI answer engines (ChatGPT, Claude, Perplexity, Google AI Mode) rather than simply ranked on a traditional search results page.
What is FAQPage Schema in the context of AI SEO?
FAQPage Schema describes one piece of the larger Generative Engine Optimization (GEO) problem — measuring and fixing how ChatGPT, Claude, Perplexity, and Gemini talk about a business. GEO differs from classical SEO because LLM answers do not return a list of links; they return a paraphrase, and the signals that get you inside that paraphrase are different.
How does AIRank measure faqpage schema?
AIRank's Observer agent queries ChatGPT, Claude, Perplexity, and Gemini daily with the prompts your customers actually use and logs every mention. The Scanner agent then walks your site the way an LLM does — 47 signals across headings, schema, entity mesh, and source trust — and flags the specific gaps driving the result.
Why does faqpage schema matter for AI visibility?
Roughly 42% of B2B buyer research now starts inside an LLM (Forrester 2026). Pages that do not satisfy the GEO signal set get paraphrased without attribution or omitted from answers entirely — a situation Aggarwal et al. (Princeton, 2023) measured as a 30-40% citation gap against pages that do.
What is the fastest way to improve faqpage schema?
Start by running a free AIRank scan to surface the three highest-leverage fixes for your domain, then ship them through the Injector agent in a single click. Most teams see their first fix land within 12 minutes of install; citation lift typically shows up in weeks two and three once assistants re-crawl the edge-rewritten HTML.
Written by
The AIRank Editorial Team
Research & editorial, AIRank
The AIRank editorial team runs the 47-point scanner, the Observer pings, and the GEO research programme every week. Writing is reviewed by the core engineers who build the Injector, Blaster, and Surgeon agents.
About the team →