E-E-A-T Signals: What Google Actually Measures and How to Build Them

E-E-A-T is not a ranking algorithm — it is a framework that informs how algorithms are designed and evaluated.

Google employs over 16,000 quality raters who manually evaluate search results using the Search Quality Evaluator Guidelines — a 176-page document that defines what makes a page high or low quality. E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is the central framework these raters use. While raters do not directly influence rankings, their evaluations train the algorithms that do.

Experience (the first E, added in December 2022) assesses whether the content creator has first-hand experience with the topic. A software developer writing about debugging techniques has experience. A content writer who researched debugging online does not. Google's systems look for signals of direct experience: specific details that only a practitioner would know, original screenshots or examples, and described outcomes from real projects.

The practical implication: generic content written by generalists — even if factually correct — ranks below experiential content written by practitioners. This is why AI-generated content without human expert review struggles in competitive search: it can synthesize existing information but cannot demonstrate the experience of having done the work.