What is E-E-A-T

Full definition

E-E-A-T is the acronym used in Google's Search Quality Rater Guidelines (SQRG) to describe the four dimensions along which human raters assess content quality:

• Experience: Does the author have real, first-hand experience of the topic? For a heating engineer, this means photographic evidence of completed jobs, case studies, and customer-facing outcomes — not generic advice copied from a manufacturer's datasheet.
• Expertise: Does the author possess the knowledge and skills expected of a qualified professional in the field? For regulated trades — Gas Safe, NICEIC, NAPIT — holding and displaying the relevant certification is a concrete expertise signal.
• Authoritativeness: Is the author or site recognised as a reference point by others in the field? For a trade business this translates to mentions in local press, links from trade associations, and a verified Google Business Profile.
• Trustworthiness: Is the content accurate, transparent about who is behind it, and safe for the user to act on? Consistent NAP (name, address, phone) data across all platforms, a clear complaints process, and absence of misleading claims all contribute.

The acronym was originally E-A-T (three factors) in earlier versions of the SQRG. Google added the first E — Experience — in the December 2022 update to the guidelines, reflecting the increased weight given to demonstrated first-hand knowledge. This was a direct response to the rise of AI-generated content that can appear expert without genuine practical experience.

Why it matters in 2026

By 2025, AI-generated content had become ubiquitous, and Google's core updates increasingly demoted thin, generically authoritative content in favour of content with traceable first-person evidence. For home-services businesses, the practical effect is that a heating engineer who publishes detailed boiler-installation case studies with before-and-after photos outperforms a generic service page — even when the latter is technically optimised.

More significantly for the AI discovery layer: Retrieval-Augmented Generation systems used by Google AI Overviews, Perplexity, and Bing Copilot select sources based on entity trust scores. A business with high E-E-A-T signals — verified credentials in structured data, consistent entity mentions, author markup, first-hand content — is more likely to be retrieved and cited in a voice or chat answer than a business with identical services but weaker trust signals.

YMYL (Your Money or Your Life) pages — which include any content about home safety, electrical work, or gas installations — are evaluated against E-E-A-T with the highest stringency.

How it works

E-E-A-T is not a single algorithmic metric; it is a composite of many signals that Google's systems correlate with human rater scores. The practical signals that contribute include:

• Structured data (LocalBusiness, Person, author, knowsAbout) that declares credentials explicitly
• sameAs links to verified professional registrations (Gas Safe, NICEIC, FMB, and others)
• Consistent NAP data across the website, Google Business Profile, and third-party directories
• First-hand content with specific project data, dates, and outcomes
• Backlinks from trade associations and local-authority supplier lists
• Author bios with verifiable credentials and licence numbers
• Review volume and recency on Google Business Profile and Checkatrade

Difference from domain authority

Dimension	E-E-A-T	Domain authority (DA)
Origin	Google Quality Rater Guidelines	Third-party metric (Moz, Ahrefs, Semrush)
What it measures	Content trust across four dimensions	Backlink profile strength
Direct ranking factor	No (indirect via rater feedback signals)	No (correlative metric, not used by Google)
Applies to	Specific pages and authors	Entire domain
Relevance to AI citation	High — entity trust influences RAG retrieval	Low — AI systems do not use DA
Improved by	Credentials, first-hand evidence, entity signals	Earning high-quality inbound links

Related terms

LocalBusiness schema, sameAs, JSON-LD.