DefinedTerm · Glossary
What is E-E-A-T
E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness. It is the evaluative framework Google's human quality raters use to assess content quality, as documented in the Search Quality Rater Guidelines. The first E — Experience — was added in December 2022 to reward first-hand, lived knowledge over purely academic expertise. Although E-E-A-T is not a direct ranking factor with a numerical score, it shapes the training data that informs how Google's ranking systems interpret topical authority, and it is the primary lens through which AI assistants decide whether a source is citable in a generated response.
Full definition
E-E-A-T is the four-dimension framework documented in Google's Search Quality Rater Guidelines (SQRG) that human raters use to score pages when evaluating the quality of search results.
Experience refers to first-hand, real-world knowledge of the topic. A plumber who documents an actual job with photos, notes complications found on-site, and explains the specific fix they applied demonstrates experience. A page that describes plumbing in generic terms does not.
Expertise refers to formal or demonstrated skill. For medical or financial topics this often means professional credentials. For trade and construction topics it means provable, deep domain knowledge — trade certifications, years of field work, or a body of detailed technical content.
Authoritativeness refers to the reputation of the page, site, or author within the topic's community. It is measured by third-party signals: mentions, citations, links, reviews, and the entity's presence in the Knowledge Graph.
Trustworthiness is the overarching dimension that encompasses all others. It includes technical signals (HTTPS, no deceptive patterns, clear authorship), factual accuracy, and consistency of information across the web.
Why it matters in 2026
E-E-A-T has become central to AI-generated answer quality for two reasons. First, the training data and reinforcement learning pipelines for large language models used by search engines weight high-E-E-A-T content more heavily when learning what correct answers look like. Second, AI citation systems — Google AI Overviews, Bing Copilot, Perplexity — prefer sources that score well on trustworthiness and authoritativeness signals, because citing a low-trust source creates liability for the AI product.
For home-services businesses — sectors classified as YMYL-adjacent (Your Money or Your Life) because advice can affect property and safety — the Experience dimension is uniquely powerful. Documenting real projects with dates, locations, and outcomes produces content that no purely generative competitor can replicate.
How it works
E-E-A-T is operationalised through three overlapping signal layers.
On-page signals: Author bylines with verifiable identity, publication and update dates, first-hand project documentation, cited sources, and correct use of structured data (Person schema on authors, LocalBusiness schema on the business).
Off-page signals: Reviews on Google Business Profile and third-party platforms, editorial mentions in trade press, backlinks from industry associations, consistent NAP across directories, and sameAs links in schema connecting the entity to Wikidata and authoritative profiles.
Entity signals: The completeness and coherence of the business entity in the Knowledge Graph — populated primarily via JSON-LD schema and GBP — determines how confidently Google and AI assistants can attribute content to a verified, real-world actor.
Difference from the original E-A-T
| Dimension | Original E-A-T (pre-Dec 2022) | E-E-A-T (Dec 2022 onward) |
|---|---|---|
| Experience | Not present | First-hand, lived knowledge required |
| Expertise | Formal credentials and depth of knowledge | Same, but demonstrable through direct experience |
| Authoritativeness | Third-party reputation signals | Same |
| Trustworthiness | Technical and factual accuracy | Same, expanded to include source transparency |
| Primary beneficiaries | Academic and professional publishers | Also tradespeople, practitioners, and SMBs |
Related terms
LocalBusiness schema, sameAs, JSON-LD.
Fuentes
Términos relacionados
- localbusiness-schema
- sameas
- json-ld