What is E-E-A-T?
E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness — Google's framework for evaluating content quality, codified in its Search Quality Rater Guidelines. The first 'E' (Experience) was added in December 2022, joining the original E-A-T from 2014. E-E-A-T is not a direct ranking signal but a meta-framework that the rater workforce uses to score search results, which then trains Google's ranking models.
For YMYL ('Your Money or Your Life') topics — finance, health, legal, e-commerce — E-E-A-T weighs heaviest. In practice, E-E-A-T rewards: visible author bylines with credentials, citations of primary sources and named experts, last-updated dates, original photography and data (not licensed stock), structured About-us and contact information, and demonstrable real-world experience with the topic (the new 'Experience' E). E-E-A-T applies equally to AI search optimization — the same signals that earn human-rater trust earn LLM citation.
Key statistics
- Google's Quality Rater Guidelines added 'Experience' to E-A-T in December 2022, making it E-E-A-T (Google QRG v2022).
- YMYL pages with strong E-E-A-T signals (named author, credentials, citations, original data) outrank thin equivalents by 2–5× in commercial verticals (Ahrefs/Semrush 2024 studies).
- LLM citation rates correlate strongly with E-E-A-T signals: pages with named author + credentials get cited 60% more often than anonymous equivalents (GEO research, 2025).
Related terms covered here
- Experience signal
- The experience signal is the evidence that a piece of content comes from genuine first-hand engagement with its subject rather than a rewrite of what already ranks — the operational form of the first ‘E’ (Experience) in E-E-A-T, and the credibility marker an AI engine most rewards because it is the one the engine cannot synthesize from other sources.
- Author entity
- An author entity is the resolvable, credentialed identity of the person credited with a piece of content — expressed through structured author schema (Person markup), a consistent bio, a sameAs graph of the author's own profiles (LinkedIn, a personal site, ORCID where relevant), and a visible credential trail.