Does FAQ schema still show rich results in Google?

Google restricted FAQ rich results in August 2023 to only government and health authority websites. However, FAQ schema still provides value because it helps Google understand your content structure, improves AI search citation eligibility, and signals content quality through structured data - even without visible rich results in SERPs.

Can FAQ schema help with AI search citations?

Yes. AI search engines like ChatGPT, Perplexity, and Google AI Overviews parse structured data to understand page content. FAQ schema explicitly marks questions and answers, making it easier for AI models to extract and cite your content. Pages with structured FAQ data are more likely to appear in AI-generated responses.

How many FAQs should you include per page?

Include 3 to 5 FAQs per page for optimal results. Each answer should be 40 to 60 words - long enough to be useful but concise enough for AI extraction. Focus on questions your target audience actually asks. Avoid stuffing dozens of low-quality FAQs, which dilutes the signal and looks spammy.

What Is FAQ Schema and How Does It Help SEO?

FAQ schema (also called FAQPage schema) is a type of structured data markup that tells search engines your page contains a list of questions and corresponding answers. It uses the Schema.org FAQPage vocabulary, implemented in JSON-LD format, to explicitly label each question-answer pair so that search engines and AI models can parse, understand, and cite your content more effectively.

For startups building content strategies in 2026, FAQ schema sits at the intersection of traditional SEO and generative engine optimization. Even though Google restricted visible FAQ rich results in 2023, the markup continues to provide significant value for AI search visibility and content structure signals.

How Does FAQ Schema Work?

FAQ schema wraps your question-and-answer content in a standardized format that machines can read. Here is a simplified example of what FAQPage schema looks like in JSON-LD:

{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is FAQ schema?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "FAQ schema is structured data markup that identifies question-and-answer content on a web page."
      }
    }
  ]
}

This code block goes in the <head> or <body> of your HTML page. Search engines crawl it and use the structured information to understand that your page contains specific questions with authoritative answers - rather than having to infer this from unstructured text.

The key distinction from regular page content is explicitness. Without schema, a search engine has to guess which text on your page is a question and which is the answer. With FAQ schema, there is no ambiguity.

Why Did Google Change FAQ Rich Results?

In August 2023, Google announced it would limit FAQ rich results to only well-known government and health authority websites. Before this change, any site with valid FAQ schema could earn expanded SERP listings showing questions and answers directly in search results - often doubling or tripling the visual footprint of a result.

Many SEO practitioners interpreted this as "FAQ schema is dead." That interpretation misses the bigger picture. Google restricted the visible rich result, not the underlying structured data signal. FAQ schema still communicates content structure to Google's systems, and it has become more important - not less - for AI search visibility.

How Does FAQ Schema Help AI Search?

This is where FAQ schema earns its value in 2026. AI search engines - including ChatGPT Search, Perplexity, and Google AI Overviews - need to extract specific, citable information from web pages. FAQ schema pre-packages your content in exactly the format these systems prefer.

The Princeton GEO research found that content with structured data and clear answer formatting saw up to 40% higher visibility in AI-generated responses. FAQ schema directly supports this by:

Marking clear question-answer pairs that AI models can extract without ambiguity
Providing concise, citable answers that fit naturally into generated responses
Signaling content quality through the effort of implementing structured markup
Creating extraction-ready content that matches how users phrase queries to AI

When someone asks Perplexity "What is FAQ schema?" - a page with explicit FAQPage markup gives the AI model a pre-structured answer it can cite with confidence. A page with the same information buried in unstructured paragraphs is harder to parse and less likely to be selected.

What Is the Connection Between FAQ Schema and E-E-A-T?

E-E-A-T - Experience, Expertise, Authoritativeness, and Trustworthiness - is Google's content quality framework. FAQ schema supports E-E-A-T signals in several ways.

First, implementing structured data demonstrates technical competence and investment in content quality. Sites that take the time to add schema markup are signaling that they care about how their content is presented and consumed - a trustworthiness indicator.

Second, FAQ schema forces you to distill your expertise into clear, concise answers. This constraint improves content quality by eliminating filler. Each answer must stand on its own as a useful, accurate response to a specific question.

Third, when combined with other structured data types - Article schema, Author schema, Breadcrumb schema - FAQ schema becomes part of a comprehensive structured data strategy that gives search engines multiple quality signals about your content and its creator.

How Should You Implement FAQ Schema?

We implement FAQ schema on every learn page and blog post at Conbersa. Here is our approach:

Choose real questions. Every FAQ should be a question your target audience actually asks. Check Google's "People Also Ask" boxes, review forums and Reddit threads, and look at what questions AI search engines surface for your topic keywords.

Write concise answers. Keep each answer between 40 and 60 words. This length is long enough to be substantive but short enough for AI models to extract cleanly. If an answer needs more depth, provide it in the body content and keep the FAQ answer as a summary.

Use JSON-LD format. Google explicitly recommends JSON-LD over Microdata or RDFa for structured data implementation. JSON-LD is easier to maintain because it lives in a separate script block rather than being interwoven with your HTML.

Validate your markup. Use Google's Rich Results Test or Schema.org's validator to confirm your FAQ schema is error-free before publishing. Invalid markup provides zero benefit.

Limit to 3-5 FAQs per page. Quality matters more than quantity. Three well-crafted FAQs that address genuine user questions are more valuable than ten generic ones. Google's own documentation warns against using FAQ schema for content that is not genuinely in FAQ format.

Does FAQ Schema Still Matter Without Rich Results?

Yes - and arguably more than before. The value of FAQ schema has shifted from visual SERP real estate to AI search optimization. According to Semrush's 2025 structured data study, pages with valid structured data (including FAQ schema) are 2.3x more likely to appear in Google AI Overviews than equivalent pages without markup.

For startups, FAQ schema is one of the highest-ROI technical SEO investments you can make. It takes minutes to implement per page, costs nothing, and directly improves your content's eligibility for AI citations. Combined with JSON-LD structured data and strong E-E-A-T signals, FAQ schema is a foundational element of any content strategy built for the AI search era.