E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness)

E-E-A-T originated in Google's Search Quality Rater Guidelines as a human-rater framework for judging whether content demonstrates first-hand experience, subject-matter expertise, domain and author authoritativeness, and overall trustworthiness. It was never a direct ranking algorithm, but it shaped what Google's ranking systems were trained to reward.

In the answer-engine era, the same signals carry over into retrieval. AI engines preferentially cite sources that show clear authorship, verifiable credentials, named expertise and a track record of accuracy, which are the same qualities E-E-A-T was built to detect. A byline with a real name and affiliation, a page with transparent sourcing, and a domain with sustained topical history all score better on E-E-A-T and, downstream, on Citation Share.

For GEO practitioners, E-E-A-T is less a checklist than a lens: content that would satisfy a skeptical human quality rater is disproportionately the content AI engines end up retrieving.