What is E-E-A-T?

What is E-E-A-T?

E-E-A-T is Google's framework for evaluating content quality, updated in December 2022 to add "Experience" to the original E-A-T. While technically from Google's Search Quality Rater Guidelines, E-E-A-T principles apply broadly to how AI systems evaluate sources.

E - Experience: First-hand, real-world experience with the topic E - Expertise: Deep knowledge and credentials in the subject A - Authoritativeness: Recognized authority and reputation T - Trustworthiness: Accurate, honest, safe, and reliable

Why E-E-A-T Matters for AI Visibility

AI systems like ChatGPT, Claude, and Perplexity face a key challenge: which sources should they trust for recommendations?

E-E-A-T signals help AI determine:

• Which content is credible enough to cite
• Whose recommendations to amplify
• What information to present as factual

Sites demonstrating strong E-E-A-T are more likely to be cited and recommended.

The Four Components

1. Experience First-hand experience with products, services, or topics.

Signals:

• Original photos, videos, or screenshots
• Personal anecdotes and case studies
• Details only someone with experience would know
• "I used this product for 6 months" > "This product has good reviews"

2. Expertise Deep knowledge demonstrated through content quality.

Signals:

• Comprehensive, accurate information
• Technical depth where appropriate
• Credentials and qualifications
• Specialized knowledge

3. Authoritativeness Recognition from others in your field.

Signals:

• Backlinks from respected sites
• Media mentions and citations
• Industry awards and recognition
• Speaking engagements, publications

4. Trustworthiness Reliability and safety of information.

Signals:

• Accurate, factual content
• Clear sourcing and citations
• HTTPS and security
• Clear contact information
• Privacy policy and terms
• No deceptive practices

E-E-A-T and YMYL Content

"Your Money or Your Life" (YMYL) topics require higher E-E-A-T standards:

• Health and medical information
• Financial advice
• Legal information
• News and current events
• Shopping/purchasing decisions

For YMYL topics, AI systems are especially cautious about source quality.

Demonstrating Experience

On-site signals:

• First-person accounts and case studies
• Original research and data
• Product demos and walkthroughs
• Before/after documentation
• Date stamps showing ongoing use

Example: Instead of "This CRM has good integrations," write "After using this CRM for 8 months with our 50-person sales team, we found the Slack integration saves 2 hours daily."

Demonstrating Expertise

Author credentials:

• Clear author bios with qualifications
• Links to author profiles and work
• Credentials relevant to content (degrees, certifications)
• Professional history and experience

Content signals:

• Technical accuracy
• Comprehensive coverage
• Original analysis and insights
• Nuanced understanding of complexities

Demonstrating Authoritativeness

Third-party validation:

• Coverage in industry publications
• Citations from other authoritative sources
• Guest posts on respected sites
• Conference presentations
• Professional association memberships

On-site signals:

• Awards and recognitions displayed
• Client testimonials from known brands
• Case studies with results
• Press mentions and media coverage

Demonstrating Trustworthiness

Technical trust:

• HTTPS encryption
• Clear privacy policy
• Accessible contact information
• Physical address (if applicable)

Content trust:

• Accurate, verifiable information
• Clear sources and citations
• Corrections when mistakes occur
• Transparent about limitations

Business trust:

• Clear pricing (no hidden fees)
• Honest product descriptions
• Customer reviews and feedback
• Returns/refund policy

E-E-A-T Implementation Checklist

Author pages:

• Author bio on all content
• Credentials and qualifications
• Photo and social links
• Other publications/work

Content quality:

• First-hand experience where applicable
• Sources cited for claims
• Comprehensive coverage
• Regular updates

Site trust:

• HTTPS enabled
• Privacy policy
• Contact page with multiple methods
• About page with company info

Authority building:

• Industry publication coverage
• Guest posting strategy
• Award submissions
• Conference participation

Common E-E-A-T Mistakes

1. Anonymous content Content without clear authorship lacks expertise signals. Always attribute content.

2. Claims without experience "Best CRM for startups" from someone who never used a CRM rings hollow.

3. Missing trust signals No contact info, hidden company details, or unclear policies hurt trustworthiness.

4. Expertise mismatch A cooking blog reviewing software lacks relevant expertise.

5. No external validation Pure self-promotion without third-party recognition limits authoritativeness.

E-E-A-T and AI Recommendations

When AI systems make recommendations, E-E-A-T effectively acts as a filter:

• High E-E-A-T sources get cited
• Low E-E-A-T sources get ignored
• Mixed signals create uncertainty

Building strong E-E-A-T is a long-term investment in AI visibility credibility.