Article and FAQ schema formats your content in a way that makes it more accessible, verifiable, and citable by AI systems like Google AI Overviews, ChatGPT, and other AI-powered search engines.
By using clean, valid JSON-LD, you help AI systems easily identify who created your content, when it was published, and what questions it answers. This structured data not only boosts indexing but also increases click-through rates.
AI tools leverage this markup to generate accurate, fact-based responses and cite trusted sources.
What Are Article and FAQ Schema?
Think of Article schema as your content’s official “author badge,” complete with the publication date, canonical URL, and a “hero image” label that machines can interpret and trust.
FAQPage schema structures your Q&A section so search engines and AI systems can easily extract and present it in their results. It should directly match what users see on the page.
Why is this important? We’ve encountered situations where the schema didn’t match the visible content, and in these cases, AI systems like Google ignored the markup. So, the schema must match the actual page content.
Why Schema Still Matters for AI Search
Although Google scaled back its use of FAQ rich results in traditional search around mid-2023, schema hasn’t lost its relevance. Its role has simply evolved—it’s now essential for AI search contexts, helping AI tools accurately cite and extract data.
For example, we worked with Kip&Co to implement Article and FAQ schema across 120 pieces of content. Within a month, those pages began appearing in AI-platform citations and Google AI Overviews, while similar pages without schema were left unseen.
Platforms like ChatGPT, Gemini, and Perplexity use structured data to identify who authored content, when it was published, and what questions it addresses. That’s why getting your schema right is crucial.
How to Add Article Schema (Step-by-Step)
Based on our experience, simpler is better when it comes to schema implementation. Here’s a typical approach:
- Add a JSON-LD block to the <head> of your article page.
- Use this template as a reference (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 (keep it under ~110 characters)
- author.name (person or organization)
- datePublished (use ISO 8601 format)
- mainEntityOfPage.@id (canonical URL)
Recommended Fields:
- image (hero image URL)
- publisher.name + logo
- description
- dateModified (update when the content changes)
After implementing schema, run your page through Google’s Rich Results Test to catch any syntax issues and confirm your markup is valid.
Proper FAQ Schema Markup
We’ve seen FAQ schema misused in various ways, like for promotional Q&As, user comments, or ads. Here’s how to do it properly:
Use FAQPage schema only for publisher-written Q&A content that’s visibly displayed on the page—never for user comments or advertisements.
Here’s a template 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>
Key Guidelines:
- Every question must appear on the page.
- Answers should match the visible content exactly (or very closely).
- Keep answers concise (around 40-60 words).
- Don’t use FAQ schema for ads or user-generated content.
Violating these rules may lead to AI platforms or Google ignoring your markup.
Time-Saving Validation Tips
To streamline validation, we recommend running schema checks through three layers:
- Google Rich Results Test – Ensure eligibility for rich results.
- Schema Markup Validator – Catch any syntax issues.
- Site-Wide Crawl (using tools like Screaming Frog or Sitebulb) – Spot missing blocks or duplicated schema.
A common mistake we’ve seen is sites having multiple FAQPage blocks (e.g., one from a plugin, one custom), which causes errors. We merged them into one to improve visibility.
Also, if your CMS uses dynamic templates, be sure the schema gets updated regularly—some templating systems leave old data, like incorrect datePublished or mainEntityOfPage.@id.
Trust Elements for Australian Audiences
For Australian clients, a few extra trust signals are crucial for both user and AI-system credibility:
- Visible byline (author name) and publication date.
- A Privacy Policy referencing Australian Privacy Principles.
- A corrections or editorial standards page (if applicable).
- Real contact details, not just a “Contact us” form.
- Schema for Organisation (with correct type and address) when relevant.
These elements help AI systems assess content credibility and trustworthiness, especially for Australian audiences.
Measuring the Impact of Schema
After implementing schema, we track its effectiveness using various metrics:
- CTR increases in Google Search Console for marked pages.
- Schema Enhancements appear without errors in GSC or other tools.
- Citations in AI platforms like ChatGPT, Perplexity, or Gemini.
- Shifts in keyword rankings in tools like Ahrefs or Semrush.
We also use Google Analytics 4 (GA4) to annotate schema changes, allowing us to compare performance before and after implementation.
What We Learned from Implementing Schema
Here are a few key lessons:
- Automation without control can backfire—be sure to regularly check schema after updates (we once saw a plugin wipe thousands of Article schema blocks overnight).
- Date fields are often overlooked but critical for trust signals (we now auto-sync dateModified with CMS updates).
- Consistency across schema implementation helps accelerate indexing and AI extraction—brands that consistently applied schema saw faster results.
Final Thoughts
Schema may not get a lot of attention, but it’s a crucial element behind AI search visibility. In our experience, the difference between “indexed” and “cited” often comes down to structured data. The websites that thrive in AI search aren’t always those with the most content, but those that provide AI tools with everything they need to interpret it correctly.
If you’re ready to optimize your site’s technical foundation and ensure it speaks clearly to both Google and AI systems, explore our AI SEO services.
