E-E-A-T

E-E-A-T stands for Experience, Expertise, Authoritativeness and Trustworthiness — Google's four-axis lens for content quality. It started as E-A-T and gained the extra 'Experience' axis in 2022. It exists because Google needed an explicit framework for the human quality raters who evaluate search results, and publishing it lets site owners optimise against the same lens. Importantly it is a rater concept, not a direct algorithmic score.

Mechanically it works through signal accumulation rather than direct scoring. E-E-A-T itself is not an algorithm variable, but Google trains and tunes ranking systems so they reward pages that match the framework. Visible authors, company information, external citations, real-world reviews, secure delivery (HTTPS) and factual accuracy stack up over time. The same family of signals appears to influence which sources LLMs feel safe quoting.

Why E-E-A-T is decisive for GEO: when AI picks sources, it leans on signals close to what human raters use. The decision process inside ChatGPT, Perplexity and AI Overviews effectively filters by authority cues — visible author, external coverage, consistent brand information. Useful KPIs are byline coverage, external citation frequency, and backlinks from authoritative domains. Villion diagnoses E-E-A-T signal gaps on a site and ranks them by upside.

Set against other quality signals the role is distinct. Schema.org is the shape of what you say, body content is what you say, E-E-A-T is who is saying it and on whose authority. The three reinforce each other. AI Overviews ride the regular search algorithm, so E-E-A-T effects are most direct there, but ChatGPT and Perplexity also weigh domain authority heavily when picking citations.

Two myths worth dispelling. First, the idea that you can compute an E-E-A-T score. There is no published number — it is a qualitative frame for reading your own pages the way a Google rater would. Second, the notion that E-E-A-T only matters for YMYL (money or health) topics. In the AI answer era, source trust matters across categories, because models propagate factual claims everywhere, not just in regulated verticals.

Sensible next steps: surface author bylines and credentials on all core content, harden About, Press and Trust pages with company authority signals, accumulate external coverage and reputable directory listings, run a regular accuracy and freshness audit, and compare E-E-A-T signal density between pages that get cited in AI answers and pages that do not. In the GEO era, E-E-A-T is the operating frame for becoming a source AI is willing to trust.