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Structured data that helps AI cite, trust, and recommend your content.
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Is your structured data speaking AI’s language — or just ticking SEO boxes?
Your schema tells AI what your page is — not just how to rank it.
Before GPT-4 or Claude reads your words, they interpret your structure. This diagnostic helps you assess whether your schema is clear, aligned, and citation-ready for today’s AI systems.
What this helps you uncover:
Are you using the right schema types for each page?
Can AI understand who it’s for and what it does?
Is your structured data retrievable — or just valid?
Why This Matters Now:
Search engines used to rank pages. AI tools like ChatGPT, Claude, and Perplexity now generate answers.
That means your structured data doesn’t just support SEO — it powers how your brand is interpreted, summarized, and recommended by AI.
If your schema is vague, missing, or misaligned:
You risk being skipped by language models.
You lose control of how your content is quoted or described.
You fall behind brands that optimize for AI retrievability — not just search ranking.
This is your chance to speak to AI in its own format — with clean, structured, explainable signals that make your content easier to trust, cite, and surface.
Page Purpose Matching & Main Entity
Interpretability & Clarity for LLMs
What types of schema are you currently using?
Define Yourself.
Audit Question:
Are you tagging content with specific schema types — or just using generic metadata?
What this means:
Schema types like Article, FAQ, Product, or HowTo tell AI what your content actually is. Defaulting to WebPage is like handing someone a box without a label.
Why this matters:
LLMs rely on your schema to interpret format and function. Vague or missing types make your page harder to summarize or rank in conversational answers.
Best Practices:
Use precise schema types for blogs, solutions, product pages, FAQs, and more
Avoid only using
WebPageas a fallbackMake your structure match your purpose
Think in terms of “what is this page for?” — and tag it accordingly
Where is your structured data added or managed?
Know Where Your Schema Lives.
Audit Question:
Do you know where your schema lives — and who controls it?
What this means:
Structured data can come from your CMS, plugins, inline HTML, or external tag managers. If you don’t know the source, you can’t fix or improve it — and AI tools may be seeing stale or incorrect versions.
Why this matters:
LLMs and search engines read your schema before your body copy. If the data is broken, outdated, or inconsistently deployed, they’ll skip you — or worse, misinterpret your intent.
Best Practices:
Store schema centrally (JSON-LD format is preferred)
Avoid relying entirely on plugins — know what’s injected
Make schema version-controlled like code
Treat structured data as part of the content publishing process
Are your schema types aligned with page content?
Name It What It Is.
Audit Question:
Do your schema types match what the content actually is?
What this means:
Using the wrong schema type (e.g. labeling a service page as an article) sends mixed signals to search engines and LLMs. Schema is meant to describe function, not just format.
Why this matters:
If schema mismatches the actual page purpose, AI tools like GPT and Bing may flag your content as untrustworthy — or skip it entirely in summaries and answers.
Best Practices:
Use schema types like
Article,FAQPage,Product, orHowToappropriatelyMatch schema type to the intent of the content
Audit templates to ensure consistency between layout and structured data
Update schema when a page’s purpose evolves
Do your schema types clearly reflect the purpose of the page?
Structure Drives Meaning.
Audit Question:
Does your schema signal what the page is really for?
What this means:
Generic schema like WebPage doesn’t help LLMs understand or prioritize your content. Purpose-aligned schema provides clarity for both search engines and AI tools about how to interpret and categorize a page.
Why this matters:
LLMs are more likely to surface, quote, or cite content that has clear, confident structure. Schema becomes your metadata “elevator pitch.”
Best Practices:
Avoid relying only on default page-level schema
Use
FAQPage,BlogPosting,Service, orProductwhere appropriateMake sure the schema name and description align with on-page messaging
Use schema to explicitly label the page’s role in your content strategy
Do you specify mainEntity or page focus fields?
Point the Spotlight.
Audit Question:
Are you highlighting the central topic of each page?
What this means:mainEntity (and related fields like about or headline) gives AI a clear signal about the core idea of the page. It’s the schema equivalent of saying: “This is what we want you to focus on.”
Why this matters:
Without it, LLMs might latch onto the wrong detail — or miss the point entirely. With it, you increase the chance of being correctly summarized, cited, or surfaced.
Best Practices:
Add a
mainEntityproperty to structured dataPoint it to a key idea, FAQ, or featured content block
Match it with supporting content like intro text or hero copy
Use schema validation tools to confirm it's in place and working
Name Your Reader.
Audit Question:
Does your schema help AI know the intended audience?
What this means:
Schema properties like audience, about, or even carefully written description fields let LLMs infer who the page is for — not just what it says. This improves targeting, relevance, and retrieval in AI-powered tools.
Why this matters:
When AI can understand who your content helps, it’s more likely to serve it up in response to user questions. Without audience signals, your page may get skipped in favor of more targeted content.
Best Practices:
Use the
audienceproperty where supported (e.g.,EducationalAudience,BusinessAudience)Include role or industry in your schema
descriptionUse phrasing like “Designed for finance leaders…” or “A guide for B2B marketers…”
Align tone and metadata with ICPs (ideal customer profiles)
Is your schema readable in plain language?
Talk Like a Human.
Audit Question:
Can a language model interpret your structured data clearly?
What this means:
Fields filled with abbreviations, developer shorthand, or internal IDs may technically validate — but they’re useless to LLMs. AI systems need human-readable text to generate accurate summaries or citations.
Why this matters:
When your schema uses plain, conversational language, models like GPT-4 or Claude can cleanly quote, cite, or interpret your content. Jargon or code breaks that flow.
Best Practices:
Avoid internal codes like
prd_XYZ_21in visible fieldsWrite out full descriptions in sentence form
Ensure
name,description, andheadlinefields are scannable by non-expertsTest summaries from GPT or Claude to see what they extract
How customized is your schema content?
Template or Tailored?
Audit Question:
Are your schemas templated — or handcrafted?
What this means:
Prebuilt plugins and CMS templates often apply the same schema across every page — even if the actual content is different. AI engines then see repetition, not relevance.
Why this matters:
Customized schema gives LLMs the specific signals they need to accurately interpret, summarize, and trust your content. One-size-fits-all doesn’t work in a prompt-driven world.
Best Practices:
Handwrite
description,headline, andaboutfields for important contentAdjust schema to reflect the purpose of each page
Don’t just validate — personalize
Match schema values to real content, not boilerplate labels
Does your schema explain key content highlights?
Don’t Bury the Lead.
Audit Question:
Are your schemas helping AI understand your main ideas?
What this means:
Structured data fields like description, headline, or about are often what LLMs read before scanning your page. If those fields are vague, abstract, or blank — your best points may go unseen.
Why this matters:
AI tools don’t just cite titles. They pull highlights. The clearer and more value-packed your schema fields, the more likely you are to be summarized accurately or quoted in full.
Best Practices:
Use
descriptionfields to capture the core takeawayMirror your top bullet points or TL;DRs in your schema
Treat schema like a table of contents: it should preview what matters
Write schema values in plain language for AI reuse
How often do you validate your schema?
Broken Schema = Broken Signals.
Audit Question:
Are you checking if your schema actually works — or assuming it does?
What this means:
Even small errors (like a missing comma or incorrect type) can invalidate your schema entirely. If Google or AI tools can’t parse it cleanly, it won’t help your visibility — and may even reduce trust.
Why this matters:
LLMs and search engines use validation as a signal of content quality. Invalid or outdated schema is often skipped or deprioritized.
Best Practices:
Use Google’s Rich Results Test and Schema.org Validator
Run validations when launching new templates or content
Build schema checks into your QA workflows
Document error fixes and structure updates for transparency
Have you tested what AI models do with your schema?
Prompt the Truth.
Audit Question:
Have you actually checked how GPT, Claude, or Bing Chat interpret your structured data — or just hoped it’s working?
What this means:
Schema isn’t just for crawlers anymore — it’s for summarizers. Tools like GPT-4 and Claude often scan schema fields first to frame answers, build citations, or explain your content.
Why this matters:
If AI tools are misreading or skipping your schema, your content may not show up in responses — or it may be inaccurately summarized.
Best Practices:
Prompt GPT-4: “Summarize [page URL] using structured data”
Cross-check: Does the response match your intent?
Update fields like
description,mainEntity, andaboutTest across Bing Chat, Perplexity, and Claude for coverage
Schema That Surfaces.
Audit Question:
Is your structured data influencing whether your content is selected, summarized, or cited in tools like GPT, Perplexity, or Bing?
What this means:
LLMs don’t just find — they choose. Schema helps AI understand context and relevance. But if your data isn’t optimized, your pages may be skipped over.
Why this matters:
This is about retrievability, not just readability. Schema that aligns with user intent and content purpose can increase your chances of being featured in AI results.
Best Practices:
Use prompt-testing tools to search for your brand or URLs
Log when structured data influences summaries
Ensure schema includes audience, purpose, and clarity cues
Review AI-generated answers for representation accuracy
Is your schema part of a larger AI visibility strategy?
Beyond SEO. Into the LLM Layer.
Audit Question:
Are you designing structured data with language models in mind — or still treating it as an SEO checkbox?
What this means:
Traditional schema was built for search engines. But today, visibility depends on how well GPT-4, Claude, Bing, and Perplexity can interpret, summarize, and reuse your structured data.
Why this matters:
LLMs aren’t crawling — they’re citing. Schema that’s tuned for clarity and context boosts your retrievability, not just your ranking.
Best Practices:
Map schema fields to the answers you want AI to reuse
Treat structured data as part of content design, not an afterthought
Use plain language and clear purpose tags
Incorporate schema planning into editorial calendars and QA
Do you tailor your schema for different formats (FAQ, HowTo, Product, etc.)?
Match the Format. Maximize the Signal.
Audit Question:
Are you aligning schema types to the actual content format — or applying the same template everywhere?
What this means:
Each page type (FAQs, articles, services, product pages) has an ideal schema type. LLMs and search engines rely on these signals to understand and present your content properly.
Why this matters:
Misused or overly generic schema can confuse AI models — or cause them to skip your content entirely. Using precise types boosts clarity, trust, and retrievability.
Best Practices:
Use
FAQPagefor question-based contentApply
HowTowhere step-by-step actions existTag
ProductorServicewhere offers are madeDon’t reuse blog schema for everything — be deliberate
Audit your site to align schema types with page purpose
Are schema updates part of your content publishing workflow?
Structure Shouldn’t Be Static.
Audit Question:
Do you regularly update your structured data when content changes — or is it “set it and forget it”?
What this means:
Schema should evolve alongside your content. If your metadata is outdated, your AI visibility decays — even if your content is great.
Why this matters:
AI systems use schema to understand and summarize your content. If your schema doesn’t reflect what’s actually on the page, models like GPT, Claude, and Bing may misinterpret or skip it.
Best Practices:
Add schema checks to your content QA checklist
Use dynamic or CMS-integrated schema generation when possible
Revalidate schema after major updates or redesigns
Make schema part of your publishing pipeline — not an afterthought
Test how AI summarizes pages after changes
Is your structured data actually working for AI — or just sitting there?
Most websites have schema.
Very few use it well.
AI systems like ChatGPT, Claude, Bing, and Perplexity now read your structured data to decide:
→ What your page is about
→ Whether it’s worth quoting
→ Who it’s for — and why it matters
If your schema is vague, off-topic, or misaligned with your content…
AI won’t cite you.
It’ll skip you.
Request a Schema Audit, you’ll get:
✔ A schema strategy map across your key page types
✔ LLM retrievability + citation-readiness score
✔ Actionable markup improvements for AI and SEO
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