E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness)
E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is Google's framework for evaluating content quality, particularly for YMYL (Your Money or Your Life) topics. Originally introduced as E-A-T in 2014, Google added the second 'E' for Experience in December 2022. E-E-A-T is now operationalized through Author Vectors — entity-resolution backbones that affect both classic SEO ranking and AI Overview citation eligibility.
Also called: EEAT, Double-E-A-T, E-A-T · Last updated: May 27, 2026 · By Joseph W. Anady
Why it matters.
E-E-A-T started as a Search Quality Rater guideline (the manual rubric Google's human raters use). Over years it became encoded into the algorithm via Author Vectors and entity scoring. By 2026 it's not a rubric — it's a measurable signal stack: who wrote the content, are they a recognized expert, what's their track record, can their identity be verified across the web?
How it works.
E-E-A-T resolves to four dimensions per content piece: (1) Experience — first-hand involvement with the topic (was the author actually there, did they do the thing), (2) Expertise — demonstrated competence (credentials, body of work, citation density), (3) Authoritativeness — recognition by others (third-party citations, awards, Wikipedia, Knowledge Graph), (4) Trustworthiness — accuracy and integrity (correct facts, transparent sourcing, no misleading claims). The signals are measured per-page and per-author.
2026 reality check.
Author Vectors are now the entity-resolution backbone for E-E-A-T scoring. Claude cites content at 94 percent confidence when Article schema declares an author entity with verified sameAs chain, versus 61 percent for plain text (Oltre.ai 2026 research). The author identity stack is no longer optional — it's a measurable ranking factor that compounds across every page the author writes.
Data points
- Introduced as E-A-T in Google Search Quality Rater Guidelines 2014
- Updated to E-E-A-T (added Experience) December 2022
- Claude cites author-attributed Article schema at 94% confidence vs 61% plain text (Oltre.ai 2026)
- Author Vector now the entity-resolution backbone (Leadgen Economy 2026 analysis)
- Author identity stack: Person schema + sameAs chain + identifier array (Wikidata QID + KG MID + ORCID minimum)
First-hand insight from ThatDeveloperGuy.
ThatDeveloperGuy operationalizes E-E-A-T through Joseph W. Anady's verified identity stack: Wikidata Q139901957, Google KG MID /g/11n57xh708, ORCID 0009-0008-8625-949X, SAM.gov UEI FFG3A4SK9HY6 (federal verification), 175 dev.to articles, Zenodo DOIs, GitHub presence, 5.0/7 verified Google reviews, DVIDS military service records. Every TDG page attributes content to Joseph via Person schema with the full sameAs chain. The Author Vector is the entity-resolution backbone for our E-E-A-T scoring.
How TDG approaches it
TDG's E-E-A-T stack: Joseph W. Anady authors all TDG content. Person schema with sameAs to LinkedIn, GitHub, ORCID, Wikidata Q139901957, Google KG MID /g/11n57xh708, dev.to, Hashnode, Medium, Crunchbase, ResearchGate, Google Scholar, AlternativeTo, StackShare, FeedTheJoe. Identifier array includes wikidataQID, googleKgMID, ORCID, SAM.gov UEI. Credentials: SDVOSB-certified, BA Computer Engineering (Colorado State), MA Cybersecurity in progress (University of Phoenix), TryHackMe WIZARD rank.
Common mistakes.
- Publishing content without named author attribution (Person schema)
- Missing sameAs chain (LinkedIn + GitHub + ORCID + Wikidata + KG MID minimum)
- Inconsistent author identity across the web (different LinkedIn vs ORCID vs site bio)
- Failing to demonstrate first-hand experience (the new 'E' added December 2022)
- Mixing author topics (publishing AEO content under one author then suddenly a gardening post weakens the Author Vector)
FAQ.
What's the difference between E-A-T and E-E-A-T?
E-A-T = Expertise, Authoritativeness, Trustworthiness (introduced 2014). E-E-A-T = adds Experience (introduced December 2022). The added 'E' emphasizes first-hand involvement — was the author actually there or did they just write about it?
Is E-E-A-T a direct ranking factor?
Google publicly says E-E-A-T is not a single algorithmic signal but rather a framework that informs many signals. In practice, the encoding of E-E-A-T into Author Vectors and entity scoring means it functions as a measurable ranking factor for content with named authors.
What is YMYL?
Your Money or Your Life — content that could impact a user's financial, health, safety, or wellbeing decisions. Google applies elevated E-E-A-T standards to YMYL content. Medical advice, financial guidance, legal information, safety information are all YMYL.
How do I demonstrate Experience for content I haven't done myself?
You can't authentically demonstrate first-hand experience you don't have. The honest path: either (a) acquire the experience and write about it, or (b) interview/cite someone who has the experience and credit them as the source. Faking experience signals is risky and often detectable.
Does E-E-A-T affect AI Overview citation?
Yes. AI Overview citations strongly favor pages with verified author entities. Claude cites author-attributed Article schema at 94% confidence vs 61% for plain text. ChatGPT favors DR80+ authority domains which correlate with high E-E-A-T sites.
Maintained by Joseph W. Anady at ThatDeveloperGuy. Back to glossary · Suggest a term