E-E-A-T
Experience, Expertise, Authoritativeness, and Trustworthiness — Google's framework for evaluating content quality, now also one of the strongest predictors of inclusion in AI-generated answers.
E-E-A-T originated in Google's Search Quality Rater Guidelines and now functions as a shared currency between traditional SEO and AI search. Language models that ground answers in retrieved web content disproportionately cite pages with strong E-E-A-T signals.
The four dimensions
- Experience — first-hand evidence the author actually used a product, visited a place, or completed the task
- Expertise — depth of subject knowledge demonstrated in the content and author bio
- Authoritativeness — recognition from peers in the field (citations, awards, named credentials)
- Trustworthiness — accuracy, transparency, secure delivery, clear corrections policy
Why E-E-A-T matters for GEO
LLMs trained on the open web inherit Google's quality signals indirectly — the same pages Google ranks well tend to be the same pages that surface in RAG retrieval. Strong E-E-A-T pages are:
- More likely to be cited by Perplexity and other answer engines
- More likely to be referenced in Google AI Overviews
- More resistant to being replaced by a competitor's page over time
Tactical checklist
- Named, credentialed authors with real bios
- Original photography, screenshots, or data — not stock imagery
- Inline citations and references to primary sources
- Last-updated date and a transparent corrections log
- Clear About, Contact, and Editorial policy pages
