E-E-A-T for AI Systems

🎯 Quick Summary

E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) signals are critical for AI citation
Originally a Google quality concept, now essential for LLM trust evaluation
AI systems prefer citing sources with strong author credentials, verified expertise, and transparent sourcing
Implementing E-E-A-T involves technical markup, content practices, and authority building

🔑 Key Concepts at a Glance

Experience: First-hand, practical knowledge demonstrated
Expertise: Domain knowledge, credentials, qualifications
Authoritativeness: Recognition as leading source in field
Trustworthiness: Accuracy, transparency, security
Schema Markup: Structured data telling AI about credentials

🏷️ Metadata

Tags: e-e-a-t, authority, trust, quality-signals Status: %%ACTIVE%% Complexity: %%MODERATE%% Max Lines: 450 (this file: 440 lines) Reading Time: 10 minutes Last Updated: 2025-01-18

What is E-E-A-T?

Origin & Evolution

Google's Quality Rater Guidelines (2014):

E-A-T: Expertise, Authoritativeness, Trustworthiness
Manual quality evaluation framework
Particularly important for YMYL (Your Money Your Life) content

2022 Update:

Added "Experience" → E-E-A-T
Emphasized first-hand knowledge
Rewarded practical demonstration

2023-2025: AI Era:

LLMs adopt similar evaluation criteria
AI systems prefer high E-E-A-T sources
Critical for citation decisions

The Four Pillars Explained

E-E-A-T Framework:

EXPERIENCE ───────┐
                  │
EXPERTISE ────────┤──→ CITATION LIKELIHOOD
                  │
AUTHORITATIVENESS─┤
                  │
TRUSTWORTHINESS ──┘

Why AI Cares About E-E-A-T

AI's Responsibility Problem

The Challenge:

User: "What medication should I take for diabetes?"
AI: [Generates answer]

If wrong → Real harm to user
If AI cites bad source → AI's credibility damaged

Solution: Prefer high E-E-A-T sources

Citation Decision Process

AI Evaluation (simplified):

def should_cite_source(content, source):
    relevance_score = check_relevance(content, query)
    if relevance_score < threshold:
        return False

    # E-E-A-T evaluation
    experience = has_firsthand_knowledge(content)
    expertise = check_author_credentials(source)
    authority = domain_reputation(source)
    trust = verify_accuracy_transparency(content)

    eeat_score = (experience * 0.2 +
                  expertise * 0.3 +
                  authority * 0.3 +
                  trust * 0.2)

    return eeat_score > citation_threshold

Result: High E-E-A-T → More citations

The Four Pillars

1. Experience (First-Hand Knowledge)

What It Is: Demonstrable practical experience with the topic.

Examples:

❌ Low Experience:

"CRM software helps businesses manage customers.
Popular options include Salesforce and HubSpot."

(Generic, no first-hand knowledge)

✅ High Experience:

"After testing 23 CRM platforms over 6 months with our
15-person sales team, we found HubSpot's contact management
most intuitive. Setup took 47 minutes vs Salesforce's 3+ hours.

Our team's average time-to-first-sale decreased 18% in the
first quarter after implementation."

(Specific, detailed, first-hand)

How to Demonstrate:

Specific numbers and timeframes
Screenshots and data
Before/after comparisons
Detailed process descriptions
Personal insights from experience

2. Expertise (Domain Knowledge)

What It Is: Verified knowledge, credentials, qualifications in the field.

Examples:

❌ Low Expertise:

About the Author:
John Smith writes about business software.

✅ High Expertise:

<div itemscope itemtype="https://schema.org/Person">
  <span itemprop="name">Dr. Sarah Chen</span>,
  <span itemprop="jobTitle">CRM Industry Analyst</span>
  <div itemprop="affiliation" itemscope itemtype="https://schema.org/Organization">
    <span itemprop="name">Software Review Institute</span>
  </div>
  <p>
    <span itemprop="description">
      15 years analyzing enterprise software. MBA from Stanford,
      PhD in Information Systems. Published 40+ peer-reviewed
      papers on CRM adoption. Certified Salesforce Admin.
    </span>
  </p>
  <a itemprop="url" href="/authors/sarah-chen">Full Bio</a>
</div>

Credentials to Highlight:

Academic degrees
Professional certifications
Years of experience
Publications and research
Speaking engagements
Awards and recognition

3. Authoritativeness (Recognition)

What It Is: Recognition as a leading/authoritative source in your field.

External Signals:

Authoritativeness Score:

Backlinks from:
├─ .edu domains: High authority
├─ .gov domains: Very high authority
├─ Industry publications: High authority
├─ Major media: Moderate-high authority
└─ Random blogs: Low authority

Media mentions:
├─ Featured in Forbes, NYT: High
├─ Industry publications: Moderate
└─ Press releases only: Low

Social proof:
├─ Verified accounts: Moderate
├─ Large following: Low-moderate
└─ Engagement quality: Moderate

Building Authoritativeness:

Guest posts on authoritative sites
Original research cited by others
Speaking at industry events
Media coverage
Expert quotes in publications
Industry awards

4. Trustworthiness (Accuracy & Transparency)

What It Is: Demonstrated accuracy, transparency, and security.

Technical Trust Signals:

✅ HTTPS (SSL certificate)
✅ Clear privacy policy
✅ Transparent contact information
✅ About page with company details
✅ Regular content updates
✅ Fact-checking and corrections
✅ Clear editorial process

Content Trust Signals:

<!-- Citing sources -->
<p>
  According to a <a href="..." rel="nofollow">2024 Gartner report</a>,
  CRM adoption increased 23% year-over-year.
</p>

<!-- Transparency about testing -->
<div class="methodology">
  <h3>Our Testing Process</h3>
  <p>We tested each CRM for 30 days with a 10-person team.
     Evaluation criteria: ease of use (40%), features (30%),
     pricing (20%), support (10%).</p>
</div>

<!-- Update dates -->
<time datetime="2025-01-15">Last updated: January 15, 2025</time>
<p>Reviewed by: Editorial Team | Fact-checked by: [Name]</p>

<!-- Disclosures -->
<div class="disclosure">
  Disclosure: We may earn affiliate commissions from some links.
  This does not affect our editorial independence.
</div>

Technical Implementation

Schema Markup for Authors

Person Schema:

{
  "@context": "https://schema.org",
  "@type": "Person",
  "name": "Dr. Sarah Chen",
  "jobTitle": "CRM Industry Analyst",
  "affiliation": {
    "@type": "Organization",
    "name": "Software Review Institute"
  },
  "alumniOf": {
    "@type": "EducationalOrganization",
    "name": "Stanford University"
  },
  "knowsAbout": ["CRM Software", "Enterprise SaaS", "Sales Technology"],
  "url": "https://example.com/authors/sarah-chen",
  "sameAs": [
    "https://linkedin.com/in/sarahchen",
    "https://twitter.com/sarahchen"
  ]
}

Article Schema with Author

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Complete Guide to CRM Software Selection",
  "author": {
    "@type": "Person",
    "name": "Dr. Sarah Chen",
    "jobTitle": "CRM Industry Analyst",
    "url": "https://example.com/authors/sarah-chen"
  },
  "publisher": {
    "@type": "Organization",
    "name": "Software Review Institute",
    "logo": {
      "@type": "ImageObject",
      "url": "https://example.com/logo.png"
    }
  },
  "datePublished": "2025-01-15",
  "dateModified": "2025-01-15",
  "description": "Comprehensive CRM software comparison...",
  "about": "CRM Software"
}

Organization Schema

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Software Review Institute",
  "url": "https://example.com",
  "logo": "https://example.com/logo.png",
  "description": "Independent software testing and analysis since 2010",
  "foundingDate": "2010",
  "address": {
    "@type": "PostalAddress",
    "addressLocality": "San Francisco",
    "addressRegion": "CA",
    "addressCountry": "US"
  },
  "contactPoint": {
    "@type": "ContactPoint",
    "contactType": "customer support",
    "email": "support@example.com"
  },
  "sameAs": [
    "https://twitter.com/softwarereview",
    "https://linkedin.com/company/softwarereview"
  ]
}

Content Best Practices

Author Bylines

Minimum:

<div class="author">
  <img src="/authors/sarah.jpg" alt="Dr. Sarah Chen">
  <div>
    <strong>Dr. Sarah Chen</strong>
    <p>CRM Industry Analyst, 15 years experience</p>
  </div>
</div>

Optimal:

<div class="author" itemscope itemtype="https://schema.org/Person">
  <img itemprop="image" src="/authors/sarah.jpg" alt="Dr. Sarah Chen">
  <div>
    <h4 itemprop="name">Dr. Sarah Chen</h4>
    <p itemprop="jobTitle">CRM Industry Analyst</p>
    <p itemprop="description">
      PhD in Information Systems, Stanford University. 15 years
      analyzing enterprise software. Published 40+ peer-reviewed
      papers. Certified Salesforce Administrator.
    </p>
    <a itemprop="url" href="/authors/sarah-chen">Full Bio</a>
    <a itemprop="sameAs" href="https://linkedin.com/in/sarahchen">LinkedIn</a>
  </div>
</div>

Citing Sources

Bad:

"Studies show CRM improves sales by 30%."

Good:

"A 2024 Harvard Business Review study of 500 companies found
that CRM implementation correlated with 29% average sales increase
over 18 months ([source](link))."

Transparency

Editorial Process Page:

/editorial-process

Our Review Methodology:
Product Testing (30 days minimum)
Feature Comparison (standardized checklist)
Expert Review (industry analyst evaluation)
User Feedback (surveys, interviews)
Fact-Checking (third-party verification)
Annual Updates (re-test and refresh)

Measuring E-E-A-T

Internal Audit Checklist

Experience:

Content includes first-hand experiences
Specific examples and data points
Screenshots, photos, or evidence
Before/after comparisons

Expertise:

Author bylines with credentials
Schema markup for authors
Detailed author bio pages
Relevant certifications mentioned

Authoritativeness:

Backlinks from .edu or .gov
Citations in industry publications
Media mentions
Speaking engagements listed

Trustworthiness:

E-E-A-T Score (Estimated)

Calculate rough E-E-A-T score:

Experience: 0-10 (self-assess specificity)
Expertise: 0-10 (credential strength)
Authority: 0-10 (external recognition)
Trust: 0-10 (technical + content transparency)

Total: 0-40
Average: /4 = 0-10

<5: Low E-E-A-T (improve urgently)
5-7: Moderate E-E-A-T (competitive)
7-9: High E-E-A-T (strong advantage)
9-10: Exceptional E-E-A-T (market leader)

Implementation:

Strategy:

🆘 Need Help?

E-E-A-T Questions:

Last updated: 2025-01-18 | Edit this page

🎯 Quick Summary​

📋 Table of Contents​

🔑 Key Concepts at a Glance​

🏷️ Metadata​

What is E-E-A-T?​

Origin & Evolution​

The Four Pillars Explained​

Why AI Cares About E-E-A-T​

AI's Responsibility Problem​

Citation Decision Process​

The Four Pillars​

1. Experience (First-Hand Knowledge)​

2. Expertise (Domain Knowledge)​

3. Authoritativeness (Recognition)​

4. Trustworthiness (Accuracy & Transparency)​

Technical Implementation​

Schema Markup for Authors​

Article Schema with Author​

Organization Schema​

Content Best Practices​

Author Bylines​

Citing Sources​

Transparency​

Measuring E-E-A-T​

Internal Audit Checklist​

E-E-A-T Score (Estimated)​

📚 Related Topics​

🆘 Need Help?​