--- language: en title: "Schema markup for AI visibility: 7 key types and…" description: "Explore seven schema families, see valid JSON-LD examples, and learn to mark up identity, articles, FAQs, and products with real data—without promising citations or results…" canonical: "https://tropk.ai/en/blog/schema-markup-for-ai-visibility-the-7-types-that-matter-and-how-to-implement-each" last_modified: "2026-10-12T16:05:24.938Z" --- # Schema markup for AI visibility: 7 key types and… Explore seven schema families, see valid JSON-LD examples, and learn how to mark up identity, articles, FAQs, and products with real data—without promising citations or special results. [Luiz Gardelin]() Published on 2026-07-01T02:34:33.000Z Categorias: How to do it [Schema markup](/en/resources/glossary/schema-org) describes entities and content using a standardized vocabulary, such as Organization, Article, and FAQPage. Markup helps systems that process it identify information, but it does not guarantee a [citation](/en/resources/glossary/ai-citation) in Google [AI Overviews](/en/resources/glossary/ai-overviews) or other assistants. [Google says](https://developers.google.com/search/docs/appearance/ai-features) there is no special schema or additional technical requirement for appearing in its AI features. Which schema to implement depends on what is on the page and the requirements of the search experience. [Structured data](/en/resources/glossary/structured-data) should reflect visible, verifiable information without inventing prices, dates, reviews, or authority. This article covers seven schema families useful for organizing identity, editorial content, questions, software, tutorials, and reviews: what each describes and how to create [JSON-LD](/en/resources/glossary/json-ld) examples. The examples use demonstration data or generic fields and need to be adapted to the page’s actual data before use. ## Key points - Schema markup describes entities and content; it does not guarantee rankings, rich results, or citations by AI assistants. - Organization, Person, and Article help represent identity and authorship; sameAs and dates should match verifiable information. - FAQPage remains in Schema.org, but Google ended FAQ rich results in May 2026. - SoftwareApplication, HowTo, and Review should reflect software, steps, and reviews that actually exist on the page; never invent ratings or testimonials. - Use JSON-LD with valid syntax, check it with the Schema.org Validator, and separately verify the types supported by the Rich Results Test. ## How schema markup organizes information for search and AI [Generative AI](/en/resources/glossary/generative-ai) systems may use web pages as sources. Schema markup adds an explicit description of information such as a company name, author, date, and frequently asked questions. This description complements the visible text; it does not replace it or determine how each system retrieves or selects a source. The documented benefit is helping search engines understand information and, for supported types, assess eligibility for specific experiences. For Google [AI search](/en/resources/glossary/ai-search), [SEO](/en/resources/glossary/seo) fundamentals and useful content still matter. Content without schema can also appear. It is important to distinguish three things: valid markup in the Schema.org vocabulary, eligibility for a Google feature, and the actual choice to display or cite a page. Markup can be correct without generating a special result. [Google’s policies](https://developers.google.com/search/docs/appearance/structured-data/sd-policies) require consistency with visible content and do not guarantee display. ## 1. FAQPage: Complete questions and answers on the page **What it is:** Schema that structures question-and-answer pairs found on the page. **Why it matters:** FAQPage describes question-and-answer pairs that readers actually find on the page. The question and answer should be self-contained, and the JSON-LD should reproduce this content. The type remains in [Schema.org](https://schema.org/FAQPage), but [Google ended the display of FAQ rich results on May 7, 2026](https://developers.google.com/search/updates#deprecating-the-faq-rich-result-feature) and removed its documentation in June. Keeping the markup does not imply a special snippet or a proven increase in citations. **JSON-LD code example:** ```json { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "O que é schema markup?", "acceptedAnswer": { "@type": "Answer", "text": "Schema markup é uma descrição estruturada de entidades e conteúdo com o vocabulário Schema.org, implementada em formatos como JSON-LD. A marcação complementa o texto visível de uma página e pode ajudar sistemas de busca a identificar empresa, autor, produto, datas ou perguntas e respostas." } }, { "@type": "Question", "name": "Schema markup garante citações em Google AI Overviews ou outros assistentes?", "acceptedAnswer": { "@type": "Answer", "text": "Schema markup não garante citações em Google AI Overviews ou outros assistentes. O Google informa que não há schema especial necessário para seus recursos de IA. A marcação precisa refletir o conteúdo visível e pode apoiar sua compreensão, mas a seleção de fontes depende dos sistemas, da pergunta e do contexto." } } ] } ``` **Implementation guidelines:** - Write complete answers of the length needed; Schema.org does not require a universal word count - The answer text in the schema should match the visible text on the page - Do not add questions that do not appear in the visible content - Vocabulary: [Schema.org FAQPage](https://schema.org/FAQPage); Google status: [official changelog](https://developers.google.com/search/updates#removing-faq-rich-result) ## 2. Organization: Your company’s identity for AI systems **What it is:** Schema that declares an organization’s existence and characteristics. **Why it matters:** Organization makes a company’s identity explicit. [Google’s documentation](https://developers.google.com/search/docs/appearance/structured-data/organization) says the markup can help systems understand administrative details and distinguish one organization from others. The `sameAs` field can point to external profiles for the same organization. This does not guarantee inclusion in the [Knowledge Graph](/en/resources/glossary/knowledge-graph) or recommendation by an [LLM](/en/resources/glossary/llm). **JSON-LD code example:** ```json { "@context": "https://schema.org", "@type": "Organization", "@id": "https://www.example.com/#organization", "name": "Nome da Empresa", "url": "https://www.example.com", "logo": "https://www.example.com/logo.png", "description": "Descrição verificável da empresa", "sameAs": [ "https://www.linkedin.com/company/perfil-oficial-da-empresa" ] } ``` **Most important field: `sameAs`** It links the declared identity to external profiles that genuinely represent the same organization. Prioritize accuracy over quantity: official profiles and relevant listings help reduce ambiguity. Do not include URLs for other companies, articles merely related to the topic, or nonexistent profiles. **Official documentation:** https://developers.google.com/search/docs/appearance/structured-data/organization ## 3. Article and BlogPosting: Signaling editorial authority to AI **What it is:** Schema that declares an article or blog post with its author, date, and category. **Why it matters for AI:** Articles with Article schema are better recognized as editorial sources. For AI systems that need to cite reliable sources, an article with an identified author, publication date, and recent modification date has more credibility than anonymous content without structure. The field `dateModified` indicates the date of a real article update. It should not be changed automatically to simulate fresh content. There is no universal [RAG](/en/resources/glossary/rag) rule that makes all assistants filter pages by this field. **JSON-LD code example:** ```json { "@context": "https://schema.org", "@type": "BlogPosting", "headline": "Título do artigo", "datePublished": "2026-04-14T09:00:00-03:00", "dateModified": "2026-04-20T10:00:00-03:00", "author": { "@type": "Person", "name": "Nome do Autor", "url": "https://www.example.com/autores/nome-do-autor" }, "publisher": { "@type": "Organization", "@id": "https://www.example.com/#organization", "name": "Nome da Empresa" }, "mainEntityOfPage": { "@type": "WebPage", "@id": "https://www.example.com/url-do-artigo/" } } ``` **Difference between Article and BlogPosting:** Use `BlogPosting` for blog posts. Use `Article` for general editorial articles. Use `NewsArticle` for news content. **Official documentation:** https://developers.google.com/search/docs/appearance/structured-data/article ## 4. Person: Building author authority for E-E-A-T **What it is:** Schema that declares information about a person—author, expert, or founder. **Why it matters:** Person identifies an author or expert and can be linked through an Article’s `author`. Authorship, experience, and verifiable sources help readers assess content; declaring a person in JSON-LD does not prove credentials or create authority on its own. For sensitive topics such as health, finance, and law, accurately describe who wrote and reviewed the content, their actual credentials, and the sources supporting the guidance. Person schema is a way to represent this identity, not a certification of [E-E-A-T](/en/resources/glossary/e-e-a-t) or a universal citation requirement. **JSON-LD code example:** ```json { "@context": "https://schema.org", "@type": "Person", "@id": "https://www.example.com/autores/nome-do-autor#person", "name": "Nome do Autor", "url": "https://www.example.com/autores/nome-do-autor", "jobTitle": "Cargo real do autor", "affiliation": { "@type": "Organization", "name": "Nome da Empresa" }, "sameAs": [ "https://www.linkedin.com/in/perfil-oficial-do-autor" ], "description": "Biografia verificável do autor" } ``` **Combined use:** Person schema is more effective when combined with Article schema. An Article’s `author` field can reference the Person schema, creating an explicit connection between the article and the expert. ## 5. SoftwareApplication: Describing software and apps **What it is:** Schema specifically for software and applications, including SaaS. **Why it matters:** SoftwareApplication describes the nature of software, its category, operating system, and—when available—real offers and reviews. It can be useful on product pages and in comparisons because it makes information about the application explicit. This does not prove that assistants prioritize software with this markup or that it is the least-used type. **JSON-LD code example:** ```json { "@context": "https://schema.org", "@type": "SoftwareApplication", "name": "Nome do Software", "applicationCategory": "BusinessApplication", "operatingSystem": "Web", "url": "https://www.example.com/produto", "description": "Descrição verificável do software", "publisher": { "@type": "Organization", "name": "Nome da Empresa" } } ``` The SoftwareApplication example omits price and reviews because these fields require real data. Complete the specific requirements for a Google feature before assessing eligibility. **Common applicationCategory values for SaaS:** - `BusinessApplication` – business tools - `SocialNetworkingApplication` – social networking - `DeveloperApplication` – developer tools - `FinanceApplication` – fintech - `MarketingApplication` – use only if the vocabulary and the markup’s consumer support the category; `BusinessApplication` is a common option for business tools **Official documentation:** https://developers.google.com/search/docs/appearance/structured-data/software-app ## 6. HowTo: Structuring tutorials for AI extraction **What it is:** Schema that structures step-by-step guides. **Why it matters:** HowTo describes a sequence of instructions and its steps. The visible text should contain the same procedure, with understandable instructions and real requirements. The type remains in Schema.org, but should not be presented as a guarantee of extraction by assistants or of a Google tutorial rich result, since Google’s HowTo feature has been discontinued. **JSON-LD code example:** ```json { "@context": "https://schema.org", "@type": "HowTo", "name": "Como implementar schema markup em uma página", "description": "Passos para representar conteúdo real em JSON-LD e conferir a marcação", "step": [ { "@type": "HowToStep", "name": "Identificar o conteúdo", "text": "Escolha o tipo Schema.org que representa as informações visíveis na página." }, { "@type": "HowToStep", "name": "Adicionar a marcação", "text": "Adicione JSON-LD com dados reais e consistentes com o conteúdo publicado." }, { "@type": "HowToStep", "name": "Conferir o resultado", "text": "Valide o vocabulário no Schema.org Validator e os tipos suportados no Rich Results Test. Confira o HTML servido na URL." } ] } ``` **When to use it:** Guides with sequential steps needed to complete a task; there is no universal three-step rule. Installation and setup tutorials, or processes with clear stages. Recipes (use the specific Recipe schema for these). **Vocabulary and status:** [Schema.org HowTo](https://schema.org/HowTo) and [official Google updates on HowTo and FAQ](https://developers.google.com/search/blog/2023/08/howto-faq-changes). ## 7. Review and AggregateRating: Credibility signals for AI **What it is:** Schema that structures reviews of products, services, or content. **Why it matters:**Review describes an individual review, while AggregateRating describes an aggregation of reviews. Markup makes it easier to identify published data, but the presence of a review does not prove quality or guarantee an [AI recommendation](/en/resources/glossary/ai-recommendation). Use only authentic reviews and ratings that match the visible content and the requirements for the [entity](/en/resources/glossary/entity) type. **JSON-LD code example:** ```json { "@context": "https://schema.org", "@type": "Review", "itemReviewed": { "@type": "Product", "name": "Nome do produto avaliado" }, "author": { "@type": "Person", "name": "Nome real do autor da avaliação" }, "reviewBody": "Substitua pelo texto de uma avaliação autêntica publicada na página." } ``` The Review example shows the structure without inventing stars, a review count, or a customer’s experience. It is not a complete Google review snippet markup: rating fields and other requirements should be added only when authentic data has been published. **Important rules:** - Reviews must be authentic – do not add fake reviews - The `ratingValue` must match what is visibly displayed on the page - For SaaS businesses, combine with SoftwareApplication schema **Official documentation:** https://developers.google.com/search/docs/appearance/structured-data/review-snippet ## Which to implement first: priority table by business type | Business type | Priority 1 | Priority 2 | Priority 3 | Priority 4 | | --- | --- | --- | --- | --- | | Blog / Publisher | Article/BlogPosting | Person | Organization, if applicable | FAQPage, if there are questions | | SaaS / Software | Organization | SoftwareApplication | FAQPage | Article | | E-commerce | Product | Organization | Authentic reviews/ratings | FAQPage, if there are questions | | Service provider | Organization | FAQPage | HowTo | Review | | Company with editorial content | Organization | Article | FAQPage | Person | **Prioritize relevance:** Organization is appropriate for an organization’s identity, but it is not required for every type of website. A personal website can use Person; an article page can prioritize Article and its author. The table is an editorial suggestion, not a proven ranking of impact on AI. ## How to validate implemented schema 1. **Rich Results Test:** https://search.google.com/test/rich-results – verifica os tipos de rich results que a ferramenta suporta; não valida todos os tipos Schema.org nem garante exibição 2. **Schema.org Validator:** https://validator.schema.org/ – ajuda a verificar o vocabulário e a estrutura da marcação 3. [**Google Search Console](/en/resources/glossary/google-search-console) – Enhancements:** lists structured data errors detected during [crawling](/en/resources/glossary/crawling) 4. **Inspect URL in Search Console:** check the [HTML](/en/resources/glossary/html-the-web-s-markup-language) Google accesses and the [indexing](/en/resources/glossary/indexing) status; crawling, indexing, and displaying a result are different stages ## Sources for further reading - [Original article by Tropk]() - [Google Search Central: AI features and your website]() - [Schema.org: FAQPage]() - [Google Search Central: FAQ rich results ending in May 2026]() - [Google Search Central: Organization]() - [Google Search Central: General structured data policies]() - [Google Search Central: Changes to HowTo and FAQ]() ## Frequently asked questions ### What is schema markup? Schema markup is a structured description of entities and content using the Schema.org vocabulary, implemented in formats such as JSON-LD. The markup complements a page’s visible text and can help search systems identify a business, author, product, dates, or questions and answers. ### Does schema markup guarantee citations in Google AI Overviews or other assistants? Schema markup does not guarantee citations in Google AI Overviews or other assistants. Google says no special schema is required for its AI features. Markup must reflect the visible content and can support its understanding, but source selection depends on the systems, the question, and the context. ### Which types of schema markup should a business implement? A business should implement the types that match its entities and published content. Organization can describe the business, Article or BlogPosting represents articles, Person identifies authors, and SoftwareApplication describes software. FAQPage and HowTo make sense when a page contains real questions and instructions. It is not necessary to use every type on the same website. ### Does FAQPage still produce FAQ rich results on Google? Google says the FAQ rich result feature stopped appearing in Search on May 7, 2026, and its documentation was removed in June. FAQPage remains a Schema.org type for representing visible questions and answers. Keeping this markup does not guarantee a special Search display or citations in AI responses. ### Is it allowed to include fictional reviews and ratings in Review or AggregateRating? Fictional reviews, ratings, or review counts should not be included in structured data. Review and AggregateRating must represent authentic reviews and information consistent with what readers find on the page. False or misleading data may violate Google’s policies and make the page ineligible for rich results. ### How do you validate schema markup JSON-LD code? To validate JSON-LD, first check the JSON syntax and vocabulary with the Schema.org Validator. Also use the Rich Results Test for features supported by Google and check the HTML served at the URL. Technical validation does not prove the data is accurate, replace a review of the visible content, or guarantee display in Search. ## About the author [Luiz Gardelin]() Luiz Gardelin is the founder of Tropk.ai and works in GEO/AEO, product vision, strategy, and growth, helping brands understand and improve their presence in AI responses. --- [All pages in Markdown]()