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.