**If your page answers questions or publishes articles, the right schema tells engines exactly what each question, answer and article is.** This check looks for FAQPage, QAPage or Article schema where it's relevant. This answer-content markup helps engines understand your structured Q&A and articles, can earn rich results, and gives AI engines clearly-labelled question-and-answer pairs that are ideal to cite.
It checks whether content that should be marked up as questions, answers or articles actually is. Specifically:
- FAQPage — for a page with a list of questions and their answers, each question/answer marked up.
- QAPage — for a single question with user-style answers (a Q&A thread).
- Article — for articles and blog posts, with headline, author and date.
- Match to content — that the schema matches the visible content and uses the right type for the page.
The relevant answer schema present passes; partial or incomplete markup is a warning; none where it clearly applies is a fail.
GEObubbly checks for FAQPage, QAPage or Article schema appropriate to the page's content. It's an extended Structured Data check that runs directly against the page's markup.
Different content calls for different schema, and three answer-oriented types matter most. FAQPage marks up a list of questions and their answers — the kind of FAQ block at the bottom of many pages — so each pair is explicitly labelled. QAPage is for a single question with community-style answers, like a forum thread. Article describes articles and blog posts with their headline, author and date. Each must match the visible content: the FAQ schema should contain the same questions and answers shown on the page, or it's a violation. The payoffs are real — historically FAQ markup could produce expandable FAQ rich results, Article markup feeds “top stories” and author/date displays, and all of it helps engines understand precisely what your content is. For GEO this is especially potent: AI answer engines are in the business of answering questions, and a page that hands them clearly-labelled question-and-answer pairs (and well-marked articles) gives them citable, self-contained units to draw from.
FAQPage schema is for a page where you, the publisher, provide a list of questions and their official answers — the typical FAQ section with question-and-answer pairs you've written. QAPage schema is for a single question that has user-contributed answers, like a forum or community Q&A thread where multiple people respond and answers can be voted on. The distinction is who's answering: FAQPage for your own authoritative answers, QAPage for a user-driven question-and-answer format. Using the right one ensures engines interpret the content's structure correctly.
FAQ rich results have changed over time — Google narrowed the FAQ rich result so it now appears mainly for well-known authoritative and government/health sites rather than all sites. Even where the visual rich result isn't shown, FAQPage schema still has value: it clearly labels your questions and answers for engines, which aids understanding and is particularly useful for AI answer engines looking for citable Q&A pairs. So it's worth including accurate FAQ markup that matches your visible FAQ, both for clarity and for GEO, even if the classic rich result eligibility is now limited.
Use Article schema (or its subtypes like NewsArticle or BlogPosting) for articles, news stories and blog posts. It lets you specify the headline, author, publish and modified dates, publisher and featured image in machine-readable form, which helps engines understand the content is an article and who's behind it. This supports author and date displays, can feed news and “top stories” features for eligible publishers, and reinforces E-E-A-T by tying the piece to its author. For any substantial written content, Article schema is the appropriate type to describe it.
Yes — this is a firm requirement. The questions and answers in your FAQPage schema must match what's actually visible to users on the page. Marking up FAQs that aren't shown, or whose answers differ from the visible text, is a structured-data violation that can cost you rich-result eligibility and is treated as misleading. The schema is meant to describe the real content, not add hidden content. Generate the FAQ schema from the same source as your visible FAQ so the two always stay in sync, which keeps the markup valid and trustworthy.
AI answer engines exist to answer questions, so content that's already structured as clearly-labelled question-and-answer pairs or well-marked articles is especially easy for them to use. FAQPage and QAPage schema hand engines self-contained Q&A units they can read and cite, and Article schema identifies the piece, its author and its date for trustworthy attribution. By describing your answer content explicitly rather than leaving it as undifferentiated prose, this markup makes your content more citable — a meaningful GEO advantage as AI search grows.