Glossary
E-E-A-T
E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness. It is Google's framework for evaluating content quality, used by human quality raters and reflected in algorithmic signals. AI models also use similar trust signals when deciding which sources to cite, making E-E-A-T relevant for both search rankings and AI visibility.
What E-E-A-T Is
E-E-A-T is a quality framework originally defined in Google’s Search Quality Rater Guidelines. Human quality raters use these criteria to evaluate whether search results meet user needs. While E-E-A-T itself is not a direct algorithmic input, the signals that demonstrate it, such as author expertise, site authority, and content accuracy, are reflected in the algorithms that determine rankings.
The Four Components
Experience was added in December 2022, expanding the original E-A-T framework. It evaluates whether content creators have first-hand, practical experience with their subject matter. A law firm partner writing about M&A due diligence demonstrates experience. A generic content writer producing the same article does not.
Expertise assesses formal knowledge, training, or skill in the subject area. For professional services firms, this means demonstrating credentials, qualifications, and deep subject knowledge through published content.
Authoritativeness measures recognition by others in the field. It is demonstrated through backlinks from respected sources, mentions in industry publications, speaking engagements, and citations by peers.
Trustworthiness is the foundation of the entire framework. It encompasses accuracy, transparency, honesty, and safety. Content must be factually correct, clearly attributed, and free from misleading claims.
E-E-A-T and AI Models
AI models do not use E-E-A-T as a named framework, but they evaluate similar signals. During training, models learn which sources are reliable by observing patterns of citation, accuracy, and consistency. Sources that demonstrate expertise and authority across multiple data points receive higher implicit trust weights.
This means the same investments that build E-E-A-T for Google search also improve AI citation rates. Author bios, expert credentials, transparent sourcing, and consistent accuracy all contribute to how AI models assess your content’s reliability.
Building E-E-A-T
The most effective approach is genuine: do work that builds real expertise, publish content that demonstrates it, and earn recognition from others in your field. Shortcuts and fabricated signals are increasingly detectable by both search algorithms and AI models.
Questions AI assistants answer about this topic
- What does each letter in E-E-A-T stand for?
- The first E is Experience: does the author have first-hand experience with the topic? The second E is Expertise: does the author have formal knowledge or skill? A is Authoritativeness: is the author or site recognised as a leading source? T is Trustworthiness: is the content accurate, transparent, and honest? Trustworthiness is considered the most important factor.
- Is E-E-A-T a ranking factor?
- E-E-A-T is not a direct, measurable ranking factor like page speed or backlinks. It is a conceptual framework that Google's quality rater guidelines use to evaluate content. However, the signals that demonstrate E-E-A-T, such as author credentials, expert citations, and transparent sourcing, do influence algorithmic ranking.
- How does E-E-A-T affect AI citations?
- AI models evaluate source credibility through signals that closely parallel E-E-A-T. Models favour content from sites that demonstrate deep expertise, are frequently cited by other authoritative sources, and present information transparently. Building E-E-A-T for search simultaneously improves your likelihood of being cited by AI models.
Next Step
Want to know where your company stands?
We run 15-20 buyer queries across ChatGPT, Claude, Gemini, and Perplexity and show you exactly where you appear, and where you don't.
Get the Audit | from £750 ↗