E-E-A-T optimization: how to prove expertise to AI

What is E-E-A-T and why does it matter for AI?

E-E-A-T stands for Experience, Expertise, Authoritativeness and Trustworthiness. Google introduced this framework as a guideline for assessing web content quality. But E-E-A-T is much more than just a Google thing: it increasingly determines which sources AI models select when generating answers.

AI models need to decide which sources are reliable. They do this by evaluating signals similar to the E-E-A-T criteria. A website with clear author information, proven expertise and external validation is selected as a source more often than an anonymous page without context. For a broader perspective on how this fits into your AEO strategy, we recommend reading our introductory article first.

Experience: prove your experience

The E for Experience is relatively new to the framework and emphasizes the importance of actual experience. AI models value content written by someone with practical experience, not just theoretical knowledge.

• Share case studies and practical examples that demonstrate your own experience.
• Use first-person perspective when writing about your own experiences.
• Add photos, screenshots and other evidence of hands-on experience.
• Reference specific projects, clients or results you have achieved.
• Describe lessons learned and nuances that only someone with experience can know.

Practical example: Experience in action

Imagine you are writing an article about implementing Schema.org markup. Instead of just explaining the theory, you add: "When we implemented Schema.org for an e-commerce client with 50,000 products last year, we discovered that Product schemas with review aggregations had the greatest impact on AI citations. Visibility in Perplexity increased by 35% within three months." This kind of specific, measurable experience weighs heavily for AI models.

Expertise: show your knowledge with structured data

Demonstrating expertise to AI models requires more than just writing good content. You also need to make your expertise explicit through structured data. The Person schema is essential for this.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Person",
  "name": "Jan de Vries",
  "jobTitle": "AEO Consultant",
  "worksFor": {
    "@type": "Organization",
    "name": "Kobalt Digital"
  },
  "description": "AEO specialist with 8 years of experience in SEO and AI optimization",
  "knowsAbout": [
    "Answer Engine Optimization",
    "Schema.org",
    "AI visibility",
    "Technical SEO"
  ],
  "alumniOf": {
    "@type": "EducationalOrganization",
    "name": "University of Amsterdam"
  },
  "sameAs": [
    "https://www.linkedin.com/in/jandevries",
    "https://twitter.com/jandevries"
  ],
  "url": "https://www.kobaltdigital.nl/team/jan"
}
</script>

The knowsAbout field

The knowsAbout field in the Person schema is particularly valuable for AEO. It explicitly tells AI models which topics an author has expertise in. Use specific, relevant terms that align with the content the author writes. This helps AI models assess the author's authority on specific topics.

Author pages: the expertise hub

Create a comprehensive author page for each author on your website. This page serves as a central hub where AI models can find all expertise information about an author. Include: a biography with relevant experience, a list of publications, certifications or education, links to external profiles and a photo. Link this page to each article via the author field in your Article schema.

Authoritativeness: build authority

Authority is not only determined by what you say about yourself, but especially by what others say about you. For AI models, external signals of authority are just as important as internal signals.

• Build backlinks from authoritative websites in your field.
• Use sameAs links in your Schema.org markup to connect your digital identity.
• Ensure listings on industry pages, directories and comparison sites.
• Publish research or original data that others cite and reference.
• Contribute to trade media, podcasts and conferences to increase your visibility as an expert.

Measuring authority: signals AI models evaluate

AI models evaluate authority based on multiple signals. The number and quality of backlinks to your content are an important signal. Mentions on Wikipedia or other authoritative sources carry significant weight. Consistency of your digital identity (the same name, title and description across all platforms) strengthens trust. The more your content is cited by other sources that AI models consider reliable, the stronger your authority signal becomes.

Trustworthiness: earn trust

Trust is the foundation of E-E-A-T and the hardest to fake. AI models evaluate trust signals at multiple levels: technical, content and organizational.

1. Ensure HTTPS on all pages, preferably with HSTS headers.
2. Clearly publish your contact details, company registration number and office address.
3. Add an up-to-date privacy policy and terms of service.
4. Show customer reviews and testimonials with verifiable sources.
5. Respond to reviews and feedback, both positive and negative.

Technical trust signals

Beyond content trust signals, there are technical signals that AI models evaluate. A valid SSL certificate is the minimum. HSTS headers show that you take security seriously. A correctly configured dateModified field in your Schema.org shows that you keep your content current. Even the presence of a structured error page (404) is a signal of a well-maintained website.

E-E-A-T checklist for your website

Use this checklist to evaluate and improve the E-E-A-T signals on your website.

1. Each author has a comprehensive profile page with biography, experience and photo.
2. Person schema with knowsAbout is implemented for each author.
3. Every article has a visible author name with link to the profile.
4. sameAs links connect your digital identity with external platforms.
5. HTTPS is active on all pages with HSTS headers.
6. Contact details are clearly visible and schema-marked.
7. Privacy policy and terms are up-to-date and findable.
8. Publication and modification dates are visible and included in Schema.org.
9. Verifiable reviews or testimonials are present.
10. Content contains references to sources and research.

Measuring and improving E-E-A-T

E-E-A-T is not a binary score but a spectrum. It is useful to regularly evaluate how strong your E-E-A-T signals are and where improvement is possible. Use our AEO scanner to assess your current E-E-A-T signals and identify concrete improvement areas.

Start with the technical signals (HTTPS, contact information, privacy policy) because these are the easiest to implement. Then work on the content signals (author information, expertise demonstration) and finally build the external signals (backlinks, mentions, sameAs links).

Key takeaways

• E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) increasingly determines which sources AI models cite.
• Experience is demonstrated through practical examples, case studies and specific results from your own experience.
• Expertise is made explicit with Person schema, knowsAbout fields and comprehensive author pages.
• Authority is built through backlinks, sameAs links, mentions on authoritative sites and publications in trade media.
• Trust is earned with technical signals (HTTPS, HSTS), transparent contact details and up-to-date legal pages.

Frequently asked questions

In a world full of AI-generated content, human expertise is the ultimate differentiator. E-E-A-T is the proof of it.