AI-Specific Checks
This check reference covers signals designed specifically for AI engine optimization. These checks help ensure your content is discoverable, attributable, and properly structured for AI systems like ChatGPT, Claude, Perplexity, and other LLM-powered platforms.
Sixth weighted dimension: These AI-specific checks contribute 20% of the AEO Score and evaluate signals built for AI discovery, attribution, and citation. Each check counts by how well the evidence supports it: checks marked Unproven are shown in your report but not scored, and if nothing in this dimension can be weighed it is left out and the other weights are scaled up.
AI Crawler Permissions
Which AI crawlers may open your pages, judged by what each crawler is for: search, user fetch or training.
AI Bot Permissions
Critical
What It Checks
Reads your robots.txt the way crawlers do (RFC 9309) and checks which AI crawlers may open the scanned page, grouped by purpose: search (OAI-SearchBot, PerplexityBot, Claude-SearchBot, Bingbot, Googlebot), user fetch (ChatGPT-User, Perplexity-User, Claude-User) and training (GPTBot, ClaudeBot, CCBot, Google-Extended, Applebot-Extended, Bytespider, meta-externalagent). Crawler names as of 2026-10-07.
Why It's Important
Search and user-fetch crawlers decide whether AI search and assistants can find and open a page. Training crawlers are a separate decision: blocking GPTBot or Google-Extended does not hide you from ChatGPT search or Google AI Overviews. A crawler with no group of its own follows the
User-agent: * rules; you do not need an explicit Allow for every bot.
Scoring Method
Pass: every search and user-fetch crawler may open the page (training blocks are fine)
Not measured: robots.txt could not be read
Fail: a search or user-fetch crawler is blocked on the page
How To Fix
Give a blocked search or user-fetch crawler its own group with
Allow: /, and repeat your private-path Disallow lines there (a named group does not inherit the * rules). Decide training access separately.
Implementation Example
# robots.txt - allow AI search, decline training (example policy) User-agent: OAI-SearchBot User-agent: Claude-SearchBot User-agent: PerplexityBot Allow: / Disallow: /admin/ User-agent: GPTBot User-agent: ClaudeBot User-agent: CCBot User-agent: Google-Extended Disallow: / User-agent: * Allow: / Disallow: /admin/ Sitemap: https://example.com/sitemap.xml
AI Opt-Out Signals
Unproven · not scored
What It Checks
Looks for unofficial opt-out tags that ask AI not to use your content, such as
noai and noimageai meta tags, or restrictive TDM policies.
Why It's Important
Major engines do not document these tags, and opting out is your choice. The check is shown so that a tag your CMS added by default does not surprise you; it does not count in your score.
Scoring Method
Pass: No conflicting opt-out signals present
Partial: Some signals present but not globally blocking
Fail: Site-wide opt-out signals detected
How To Fix
Review your HTML head section and remove opt-out tags you did not intend. Check CMS/platform settings. Keep them if opting out is what you want.
What to Avoid
<!-- Unofficial opt-out tags: remove them if you did not intend to opt out --> <meta name="robots" content="noai"> <meta name="robots" content="noimageai"> <!-- No opt-out tags, standard robots --> <head> <meta name="robots" content="index, follow"> </head>
llms.txt File
llms.txt is an optional summary file proposed for AI systems. Google does not use it and no major AI engine is shown to, so these two views of one check are shown for information and are not scored.
llms.txt Presence
Unproven · not scored
What It Checks
Looks for a plain-text llms.txt file at the root of your domain (e.g., example.com/llms.txt).
Why It's Important
It is an optional proposal, not a standard that search or AI engines are known to follow. A missing file is not a problem and does not lower your score.
Scoring Method
Pass: llms.txt exists and is accessible
Fail: llms.txt not found (404) or inaccessible
How To Fix
If you want one, create a plain-text llms.txt in your website root with a title, a short description and links to your key pages.
Basic llms.txt Example
# Your Organization Name > What your organization does ## Key pages - [About](https://yoursite.com/about): Company overview - [Products](https://yoursite.com/products): Our offerings - [Blog](https://yoursite.com/blog): Latest insights
llms.txt Completeness
Unproven · not scored
What It Checks
Rates how complete the file looks: a title header, a description, URLs, two or more sections and more than 500 characters of text.
Why It's Important
A fuller file gives a reader more to go on, but no AI engine is shown to use it. Shown for information; it does not count in your score.
Scoring Method
Pass: 50 or more (title 25, description 25, URLs 25, sections 15, length 10)
Fail: below 50 or no file
How To Fix
Add the missing parts (title, description, links to key pages) if you publish the file. Keep it updated as your site changes.
Complete llms.txt Example
# TechCorp Solutions > Enterprise software company specializing in AI-powered business intelligence and data analytics. ## Key pages - [About](https://techcorp.com/about): Company overview and mission - [Products](https://techcorp.com/products): Our software solutions - [Pricing](https://techcorp.com/pricing): Plans and pricing information - [Blog](https://techcorp.com/blog): Latest insights and tutorials ## Contact - Email: info@techcorp.com - Support: support@techcorp.com
AI Readability
Optimizing content structure and format for AI system comprehension.
AI-Readable Summaries
High
What It Checks
Detects the presence of clear, concise summaries at the beginning of content (TL;DR, executive summary, key takeaways) that AI systems can quickly extract and cite.
Why It's Important
AI systems often cite the most accessible, clearly-stated information. Summaries provide quotable content that's likely to appear in AI responses.
Scoring Method
Pass: Clear summary or key points at content start
Partial: Summary exists but is buried or unclear
Fail: No summary, content requires full reading
How To Fix
Add a summary section at the top of your content. Use clear labels like "Key Takeaways" or "In Brief".
Implementation Example
<article>
<h1>Complete Guide to API Security</h1>
<aside class="key-takeaways">
<h2>Key Takeaways</h2>
<ul>
<li>API security requires authentication and encryption</li>
<li>OAuth 2.0 is the recommended standard</li>
<li>Rate limiting prevents abuse</li>
</ul>
</aside>
<!-- Main content follows -->
</article>
Semantic Markup Density
Retired
What It Checks
Retired. This check no longer runs, so it does not appear in reports and has no effect on your score. Semantic HTML is still looked at by the Semantic HTML5 check in the Structural dimension.
Why It's Important
Semantic HTML helps tools tell navigation from main content. See the Structural checks for the version that is still measured.
Scoring Method
Pass: >60% of structural elements are semantic HTML5
Partial: 30-60% semantic element usage
Fail: <30% semantic elements (mostly div/span)
How To Fix
Replace generic div elements with appropriate semantic HTML5 elements like header, main, article, section, aside, nav, footer.
Semantic HTML5 Structure
<!-- GOOD: Semantic HTML5 elements -->
<header>
<nav aria-label="Main navigation">...</nav>
</header>
<main>
<article>
<header>
<h1>Article Title</h1>
<time datetime="2024-10-15">October 15, 2024</time>
</header>
<section id="introduction">
<h2>Introduction</h2>
<p>Content here...</p>
</section>
</article>
<aside aria-label="Related content">
<h2>Related Articles</h2>
</aside>
</main>
<footer>...</footer>
Entity Recognition
Help AI systems identify and understand the entities in your content.
Entity Disambiguation
Unproven · not scored
What It Checks
Looks for links to Wikipedia, Wikidata or DBpedia pages in the content, and for
sameAs properties in your structured data that point there.
Why It's Important
Many businesses have no Wikipedia or Wikidata entry, and one is not required to appear in AI answers. Shown for information; it does not count in your score.
Scoring Method
Pass: Entities clearly identified with context, Schema.org markup
Partial: Some disambiguation but inconsistent
Fail: Ambiguous entity references throughout
How To Fix
Use full entity names on first reference. Add Schema.org markup for people, organizations, and places.
Implementation Example
<!-- Good: Clear entity disambiguation --> <p> <a href="https://www.apple.com">Apple Inc.</a> announced new products. CEO <a href="https://en.wikipedia.org/wiki/Tim_Cook">Tim Cook</a> presented the keynote. </p> <!-- With Schema.org markup --> <span itemscope itemtype="https://schema.org/Person"> <span itemprop="name">Dr. Sarah Chen</span>, <span itemprop="jobTitle">Chief AI Researcher</span> </span>
Content Licensing
Unproven · not scored
What It Checks
Detects clear licensing information using Schema.org license property or Creative Commons markup. Checks for machine-readable license declarations.
Why It's Important
A clear license tells people how your content may be reused. It is not linked to search or AI visibility, so it is shown for information and does not count in your score.
Scoring Method
Pass: Machine-readable license in Schema.org or CC markup
Partial: License mentioned but not machine-readable
Fail: No licensing information present
How To Fix
Add license information using Schema.org or Creative Commons markup in your JSON-LD structured data.
Machine-Readable Licensing
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "Article",
"headline": "Understanding AI in 2024",
"author": {
"@type": "Person",
"name": "Dr. Sarah Chen"
},
"datePublished": "2024-10-15",
"license": "https://creativecommons.org/licenses/by/4.0/",
"copyrightHolder": {
"@type": "Organization",
"name": "TechCorp Research"
},
"copyrightYear": 2024
}
</script>
Knowledge Graph Readiness
Prepare your content for inclusion in AI knowledge graphs and databases.
Knowledge Graph Readiness
Unproven · not scored
What It Checks
A rough count of the Schema.org types on the page and of capitalised names in the text.
Why It's Important
A knowledge-graph or Wikipedia entry is not a requirement for appearing in AI answers, and a count of names says little about quality. Shown for information; it does not count in your score.
Scoring Method
Pass: Clear factual statements, Schema.org types, defined relationships
Partial: Some structured content but missing connections
Fail: Narrative-only content with no extractable facts
How To Fix
Structure content with clear facts. Use definition lists, tables, and Schema.org markup. Make relationships explicit.
Knowledge Graph Ready Content
<dl itemscope itemtype="https://schema.org/Organization"> <dt>Company Name</dt> <dd itemprop="name">TechCorp Inc.</dd> <dt>Founded</dt> <dd itemprop="foundingDate">2010</dd> <dt>Headquarters</dt> <dd itemprop="location">San Francisco, CA</dd> <dt>Employees</dt> <dd itemprop="numberOfEmployees">500</dd> </dl>
Content Attribution
Make your content attributable and verifiable for AI citation.
Claim Attribution
High
What It Checks
Verifies that factual claims, statistics, and quotes are properly attributed to sources. Checks for citation markup and links to original sources.
Why It's Important
AI systems evaluate content credibility based on source attribution. Properly attributed claims are more likely to be cited. This builds E-E-A-T signals.
Scoring Method
Pass: Statistics and claims have clear sources, linked where possible
Partial: Some attribution but incomplete
Fail: Unattributed statistics and claims
How To Fix
Always cite sources for statistics and claims. Use the <cite> element and link to original sources.
Proper Attribution Example
<!-- Properly attributed claim -->
<p>
AI adoption increased by 40% in 2023, according to
<cite>
<a href="https://mckinsey.com/ai-report">
McKinsey's Global AI Survey
</a>
</cite>.
</p>
<!-- Quote with attribution -->
<blockquote cite="https://example.com/interview">
<p>"AI will transform every industry."</p>
<footer>— <cite>Dr. Sarah Chen</cite></footer>
</blockquote>
Content Versioning
Medium
What It Checks
Detects clear version indicators, publication dates, and modification timestamps. Checks for Schema.org datePublished and dateModified properties.
Why It's Important
AI systems prefer fresh, updated content. Clear versioning helps AI understand content currency and prevents citation of outdated information.
Scoring Method
Pass: Clear publish date, last updated date, and/or version number
Partial: Only publish date or ambiguous dating
Fail: No date/version information
How To Fix
Add clear publication and modification dates using machine-readable formats. Include version numbers for technical documentation.
Date/Version Example
<article>
<header>
<h1>API Security Guide</h1>
<div class="article-meta">
Published: <time datetime="2024-01-15">January 15, 2024</time>
| Updated: <time datetime="2024-10-20">October 20, 2024</time>
| Version: 2.1
</div>
</header>
</article>