Article and FAQ schema make your content readable, verifiable, and citable by AI search systems like Google AI Overviews, AI Mode, and ChatGPT.
Clean, valid JSON-LD improves indexing and click-through rates by clarifying who wrote your content, when it was published, and what questions it answers.
AI tools use this structured data to extract short, factual responses and cite trustworthy sources.
This guide explains how to add and validate both Article and FAQ schema using correct fields, examples, and Australian trust signals. This is what we'll cover:
- What Are Article and FAQ Schema?
- Why Schema Still Matters for AI Search
- How to Add Article Schema (Step-by-Step)
- How to Mark Up FAQ Schema Properly
- Validation Tips That Save Time
- Australia-Specific Trust Elements
- Measuring the Impact
- What We Learn While Implementing Schema
- Final Word
What Are Article and FAQ Schema?
Think of Article schema as giving your page the author badge, publish date stamp, canonical URL and hero image label, in ways machines can parse and trust.
FAQPage schema is your Q&A section turned into structured data that mirrors exactly what users see, questions you wrote, answers your team gave.
The reason this matters: We found situations where the visible text and the schema didn’t match and Google (or downstream AI-engines) ignored the markup.
So yes: your schema must reflect the on-page reality.
Why Schema Still Matters for AI Search
Since around mid-2023, we’ve seen that Google has scaled back FAQ rich results in traditional search.
That doesn’t mean the schema has lost value. If anything, its role shifted: now it’s foundational for citation, extraction and trust signals in AI-search contexts.
We’ve learned that structured data actually makes a difference when content is going to be cited by AI-search tools.
A real example: Our client Kip&Co, we helped roll out Article + FAQ schema across 120 pieces of content and within four weeks saw those pages start appearing in AI-platform citations and GoogleAI Overviews, while similar pages without schema stayed invisible.
With platforms like ChatGPT, Perplexity and Gemini reading structured data to decide who wrote what, when and what questions did you actually answer, you’ll want your markup in order.
It clarifies who authored the content, when it was published and updated, what Q&As you’ve provided, and that matters in machine logic.
How to Add Article Schema (Step-by-Step)
From our experience, simpler equals faster wins. Here’s how we typically implement it:
Add one JSON-LD block to the <head> of your article page.
Here’s a typical template we used for a lifestyle brand:
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "Article",
"mainEntityOfPage": {"@type": "WebPage", "@id": "https://example.com/post"},
"headline": "Implementing Article Schema for AI Search",
"author": {"@type": "Person", "name": "Jane Smith"},
"datePublished": "2025-11-04",
"dateModified": "2025-11-04",
"image": ["https://example.com/hero.jpg"],
"publisher": {
"@type": "Organization",
"name": "Brand Pty Ltd",
"logo": {"@type": "ImageObject", "url": "https://example.com/logo.png"}
},
"description": "How to add Article schema that AI systems can extract and cite."
}
</script>
Required Fields
- headline (we keep it under ~110 characters)
- author.name (person or organisation)
- datePublished (ISO 8601 format)
- mainEntityOfPage.@id (canonical URL)
Recommended Fields
- image (hero image URL)
- publisher.name + logo
- description
- dateModified (update this whenever you change content)
After implementation, run your page through Google Rich Results Test.
That catches syntax issues and helps ensure your markup is “eligible”.
How to Mark Up FAQ Schema Properly
We’ve seen FAQ schema mis-used a lot (promotional Q&As, comment threads, etc.).
Here’s how we’ve used it for clients and what we learned.
Use FAQPage schema only for publisher-written Q&A content that appears visibly on the page. Don’t tag up user comments, reviews, or ads as FAQs.
Here’s an example we used for a B2B-tech blog:
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [{
"@type": "Question",
"name": "What is FAQ schema?",
"acceptedAnswer":{"@type":"Answer","text":"It is structured data that labels your Q&A pairs so search engines and AI tools can easily extract them."}
},
{
"@type": "Question",
"name": "Should answers match the visible text?",
"acceptedAnswer":{"@type":"Answer","text":"Yes. Schema must reflect the exact wording users see for it to remain valid and trustworthy."}
}]
}
</script>
Important rules we’ve learned:- Every question must appear on the page.
- Answers must match the visible content exactly (or very closely).
- Keep answers short (we aim for ~40-60 words).
- Don’t use FAQ schema for ads or user-comments.
If you violate these, the AI-platforms or Google may ignore your markup.
Validation Tips That Save Time
From our campaigns, we always run schema validation through three layers:- Google Rich Results Test– to check eligibility for rich results.
- Schema Markup Validator – to catch broader syntax issues.
- Site-wide crawl using tools like Screaming Frog or Sitebulb – to spot missing blocks, duplicates, or misplaced schema.
Here’s a frequent mistake: we once inherited a site with two FAQPage blocks per page (one from a plugin, one custom).
That doubled up markup and caused errors that reduced AI-visibility. We merged them into one.
Another tip: If your CMS uses many templates (especially with dynamic content), check that the schema block gets updated, some templating systems leave old data in datePublished or miss mainEntityOfPage.@id.
Australia-Specific Trust Elements
When working with Australian brands, we’ve found a few trust signals matter more than global best-practice alone. These help reinforce credibility for both users and AI-systems.For our Aussie clients we implement:
- A visible byline (author name) + publish date + last-updated date.
- A Privacy Policy page referencing the Australian Privacy Principles.
- A corrections or editorial-standards page (if relevant).
- Real contact details (not just a “Contact us” form).
- Schema for Organisation (with correct @type, address etc.) if relevant.
They assist AI tools that try to assess “who is behind this content?” and “is this trustworthy?”.
Measuring the Impact
We don’t just implement the schema and leave it. We track metrics and annotate changes. Here’s how we tell when schema is doing its job:
- CTR rises in Google Search Console for marked pages.
- Schema Enhancements appear without errors (in GSC or other tools).
- We get citations in ChatGPT, Perplexity or Gemini (manual spotting).
- Keyword shifts in tools like Ahrefs or Semrush that align with new Q&A- or intent-targeted pages.
We also annotate schema changes in GA4 so we know exactly when the markup went live and can compare pre/post results.
What We Learned While Implementing Schema
- Automation without control backfires. We had a client plugin update wipe thousands of Article schema blocks overnight. Manual checks still matter.
- Date fields are invisible risks. Overlooked dateModified fields eroded trust signals, so we now auto-sync that with CMS updates.
- Consistency wins. Brands that applied consistent schema templates across all articles, FAQs and product pages (for example our work with Hush Puppies) saw faster indexing and AI-extraction success.
Schema doesn’t get much glory, but it quietly does the heavy lifting behind AI search visibility.
From our experience at StudioHawk, the difference between “indexed” and “cited” often comes down to structure. The websites that win in AI search aren’t always the ones publishing the most content, but the ones giving machines everything they need to interpret it.
If you’re ready to tighten up your technical foundation and make sure your site speaks fluently to both Google and AI systems, learn more about our AI SEO services.
