Understanding E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness)
E-E-A-T is Google's framework for evaluating content quality, originally introduced as E-A-T (Expertise, Authoritativeness, Trustworthiness) and expanded in December 2022 to include Experience.
The Four Components
- Experience: First-hand or life experience with the topic—did the author actually do the thing they're writing about?
- Expertise: Formal knowledge and skill—does the author have credentials or demonstrated competence?
- Authoritativeness: Reputation as a go-to source—is this site/author recognized in their field?
- Trustworthiness: Accuracy and reliability—can users trust the information presented?
E-E-A-T and AI Systems
AI systems like ChatGPT and Perplexity must decide which sources to cite. They use similar quality signals—content with strong E-E-A-T is more likely to be cited as a trusted source in AI-generated responses.
Building E-E-A-T for Press Releases
Press releases can demonstrate E-E-A-T through:
- Credentials in executive quotes ("Dr. Jane Smith, PhD")
- Verifiable statistics with sources
- First-hand case studies and outcomes
- Schema.org markup (Person, Organization)
Key Takeaways
- E-E-A-T = Experience + Expertise + Authoritativeness + Trustworthiness
- Experience was added in December 2022 (formerly E-A-T)
- AI systems use E-E-A-T signals for citation decisions
- Schema.org markup can encode E-E-A-T for machines
- Strong E-E-A-T increases citation likelihood