E-E-A-T

E-E-A-T is an acronym from Google’s quality guidelines: Experience (first-hand experience), Expertise (specialist knowledge), Authoritativeness (recognition by others) and Trustworthiness (reliability). It describes how a system recognises whether content is first-hand, technically sound, treated by others as a reference and reliable. The same signals apply to AI systems — at page level and at brand level.

How does E-E-A-T work in AI search?

A language model does not assess credibility in the human sense; it recognises patterns that go along with credibility. On the page, these are a named author with role and bio, sources and evidence, a publication and modification date, concrete figures instead of claims, and original content such as your own studies or measurement series. Off the page, they are mentions in trade media, reviews, consistent company profiles and references from other domains. The interplay of these signals makes a source citable.

Why does E-E-A-T matter for AI visibility?

Because an AI system chooses between several passages that answer the same question and evaluates the source in the process. Named experts are cited more often than anonymous editorial teams; statements with evidence more often than statements without. And because AI answers inherit the errors of their sources, the systems prefer sources that have proven reliable in the past — which builds up over time and via third-party sites.

What does this mean for your website?

Make authorship and expertise visible: transparent author bios, sources, evidence — on the page and in the schema. Increase fact density: statistics, data, studies, concrete years. Publish original content and your own data (information gain). And build the external recognition that no page can generate on its own (digital PR, domain authority).