Roshanak Kavian
Co-Founder and CEO of Indigo Mars, a Los Angeles marketing agency.
E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness — a framework Google originally built to guide the human quality raters who help train its search algorithms. It's become central to E-E-A-T AI search visibility too, because AI systems face the same underlying problem Google's raters were built to solve: deciding which sources actually deserve to be trusted and cited, out of an enormous and growing volume of content.
What Each Letter Actually Means
Experience asks whether the content shows genuine, firsthand involvement with the topic — not just knowledge about it, but having actually done it.
Expertise looks at real knowledge, credentials, or demonstrated skill behind the content.
Authoritativeness measures recognition by others in the field — is this a known, respected source, not just a confident one.
Trustworthiness covers accuracy, honesty, and reliability. Google's own documentation treats this as the most important of the four: without trust, strong experience or expertise on their own don't carry the same weight.
An Important Clarification Worth Getting Right
E-E-A-T is not a literal ranking factor plugged directly into an algorithm — there's no single "E-E-A-T score" attached to a page. It's a quality framework used to train and evaluate the systems that do the ranking. That distinction matters practically: chasing superficial E-E-A-T signals without the substance behind them rarely works, because the underlying systems are built to detect the difference between genuine credibility and its appearance.
Why This Applies Directly to AI Search
AI platforms don't use the term "E-E-A-T," but they apply strikingly similar underlying logic. Named authors with real, verifiable backgrounds, specific details only someone with direct experience would know, and content that gets maintained and updated over time all measurably improve how often a source gets cited in an AI-generated answer. Recent research following a major search algorithm update found that sites publishing original data and direct experience saw meaningful visibility gains, while generic, AI-paraphrased content lost a substantial share of its traffic in the same period.
What This Actually Looks Like in Practice
A real, named author with a genuine, verifiable bio — not "Admin" or a generic company byline
Specific, concrete details that demonstrate direct involvement, not general knowledge repeated from elsewhere
Consistent credential and identity information across every platform a business publishes on
Content that gets revisited and updated, not published once and left to go stale
Why This Matters More for Some Topics Than Others
Content touching health, finance, safety, or legal topics — often called YMYL, for "Your Money or Your Life" — faces a meaningfully stricter standard than lower-stakes content. A blog post about ad copy formulas has real but modest trust requirements; a post about ADA compliance risk or dental patient care has a much higher bar, since getting it wrong carries real consequences for the reader.
Where to Go From Here
Named, verifiable authorship is one of the more direct ways to signal this — it's exactly why every post in this guide carries a real name and role, not a generic byline. See the broader trust signals covered in How AI Overviews and ChatGPT Decide What to Cite.
Not sure whether your own content currently signals real experience and expertise, or just claims it? Get in touch and we'll take an honest look.
