E-E-A-T: experience, expertise, authoritativeness, trust

TL;DR

E-E-A-T is not a ranking algorithm, it is the rubric human quality raters use to train Google's models. The signals that map to it are concrete: named authors with real bios and Person schema, visible editorial standards, a corrections policy, and citations that link to primary sources. Ship all four and the brand becomes eligible for ranking on the topics Google treats as consequential.

The surface, in one paragraph

E-E-A-T stands for experience, expertise, authoritativeness, and trust. It comes from Google's Search Quality Rater Guidelines, a 175-page document that trains the humans who evaluate search results. E-E-A-T is not a direct algorithmic signal, but every ingredient in it (author identity, credentials, cited sources, editorial policies, first-person experience with the topic) maps to signals the algorithm can detect. Google weighs E-E-A-T heaviest on YMYL topics: your money, your life. Health, finance, safety, legal, and civic content are graded strictly on E-E-A-T. On those topics, a site without visible author identity and editorial standards does not rank, regardless of how well it is otherwise optimized. On non-YMYL topics, E-E-A-T is a tie-breaker that matters more every year as AI-generated content floods the index.

Where this fits in Search Everywhere Optimization

E-E-A-T is the gate on multiple surfaces.

SEO gates on YMYL. Without E-E-A-T signals, health and finance sites do not rank at all. E-E-A-T is table stakes on those topics.

AEO cites named experts. Claude, ChatGPT, and Perplexity prefer sources with clear author identity. Anonymous content loses citations.

GEO signals lean on trust. Generative engines penalize content with no attribution. Author identity is a prerequisite for citation.

KGO reinforces E-E-A-T. A Wikidata QID for the author is a credential the engines can verify. The signals loop.

VSO and VDO benefit indirectly. Assistants and video engines both prefer answers from named authoritative sources.

The five levers

1. Named authors with real Person schema

Every editorial page attributes to a real Person. The author has a bio page. The bio page has a Person schema block with jobTitle, worksFor, alumniOf, url, image, sameAs (LinkedIn, Wikidata, Google Scholar, ORCID where relevant), knowsAbout for topical areas, and description. The article page's Article schema author field references the Person by @id. This is the single strongest E-E-A-T signal Google can machine-read.

2. First-person experience woven into content

The extra E in E-E-A-T is "experience." Content should show that the author has actually done the thing. First-person accounts, specific dates, personal photos of the work, honest mistakes, real numbers. AI-written pages can pass expertise but rarely pass experience. This is the wedge that separates human-authored content from generated content in Google's evaluation.

3. Editorial standards page

A visible editorial page that documents how the site produces content. Sourcing standards, expert review process, fact-check policy, use of AI (disclosed), advertising and sponsorship boundaries, conflict-of-interest disclosure. This page signals that the publisher takes editorial responsibility. Content farms never publish these; real publishers always do.

4. Corrections policy that gets used

A corrections page that explains how errors are reported and fixed, with a public log of corrections. Corrections mark the article visibly. This is a strong publisher signal. It reads to Google as an editorial system, not a content dump.

5. Citation discipline linking to primary sources

Every factual claim links to a primary source. Not another blog post that cites another blog post. The primary source: peer-reviewed paper, SEC filing, government dataset, court document, official press release. Citations at the paragraph level, formatted as visible in-text links. This is what separates authoritative content from aggregated content.

First 30 / 60 / 90 days

Days 1 to 30: audit and standards

Author audit. Who writes for the site. Do they have bio pages. Do bios have credentials. Do bios have sameAs to LinkedIn and (where relevant) Wikidata, ORCID, Google Scholar.

Article schema audit. Does every article's Article schema attribute to a Person entity. Is publisher schema correct.

Editorial standards audit. Is there an About page, an editorial standards page, a corrections policy, an advertising disclosure. If yes, are they current.

Citations audit. On the top 20 articles, do factual claims link to primary sources. What percentage of external links go to authoritative domains.

Ghost content audit. Any AI-generated or ghostwritten content presented under real bylines. Any stock-photo author avatars. Any fake experts.

Deliverable at day 30: a bio rewrite scope, a Person + Article schema rollout plan, an editorial standards and corrections policy draft, and a citations remediation queue.

Days 31 to 60: publish and structure

Bio pages built or rewritten. Each named author gets a 500 to 1,500 word bio with credentials, published works, sameAs cluster, headshot, and topical focus.

Person schema and Article schema shipped across the site. Article-to-Person authorship linked by @id.

Editorial standards page, corrections policy, advertising disclosure page published. Footer links to all three added.

Citations remediation on the top 20 articles. Primary-source links added. Secondary-source citations trimmed.

Ghost content decisions. Anonymous articles either get a real author attached or get removed. Fake bios are killed.

Deliverable at day 60: named authorship live, published editorial standards, cleaned top 20 citations, and a governance model going forward.

Days 61 to 90: expand and monitor

Citations discipline expanded to the top 100 articles. Editorial calendar updated to require primary-source links on any factual claim.

Author authority-building. LinkedIn cadence, guest bylines on peer publications, conference appearances, book reviews or interviews.

Corrections tracking. Log of every correction, published on the corrections page.

AI-content disclosure. Where AI-assisted content exists, disclose it in the article and in the editorial policy.

Deliverable at day 90: a working E-E-A-T program with named authors, a documented editorial system, a citations standard, and monitoring in place.

Tools I use

• Google Search Console. Rich result reporting, manual action alerts, YMYL topical performance.
• Google Search Quality Rater Guidelines. The rubric itself. Cited chapter and verse when arguing internally.
• Rich Results Test. Article and Person schema validation.
• Schema.org validator. Deeper structured data checks.
• ScreamingFrog SEO Spider. Site-wide author and citation audit.
• Ahrefs or Semrush. Backlink profile of author bios, competitor E-E-A-T posture.
• Wikidata. Author entity creation and sameAs linking.
• ORCID, Google Scholar, LinkedIn. Author verification and credential exposure.
• Trust Project or NewsGuard. Independent editorial-standards validation for publishers.
• Perplexity and Claude. Verify that AI engines cite the brand's authors on the target topics.

What kills the program

Ghost bylines. Fake authors, AI-generated bios, stock-photo headshots. Google detects the pattern and drops the site.

Undisclosed AI content. AI-assisted writing is fine when disclosed and edited by a human. Publishing AI content under a real byline without disclosure kills trust the moment it is discovered.

Citations that lead nowhere. Linking to other content farms, or to broken URLs, undoes the citation discipline entirely.

No editorial standards page. On YMYL topics, the absence of an editorial page is itself a downweight signal.

Weak About page. A stock-photo team page with no credentials, no addresses, and no verifiable information reads as low trust.

Marketing content masquerading as editorial. Sponsored content without disclosure violates FTC guidelines and Google policy simultaneously.

Author bios that are marketing copy. "Passionate about helping our customers succeed" is not a credential. Real credentials, real dates, real prior positions.

KPIs that matter

• Named author coverage. Percent of editorial pages attributed to a real named Person.
• Person schema completeness. Percent of author bios with full schema and sameAs coverage.
• Citation density. Average number of primary-source citations per article.
• AI answer engine citation rate. Manual sampling on target topical queries.
• YMYL ranking coverage. Position and impressions on high-trust queries.
• Corrections logged. Number of corrections issued in the current quarter. (Non-zero is healthy.)
• Author authority index. Composite: bylines on external publications, speaking slots, backlink profile to bios.

FAQ

What does the extra E in E-E-A-T stand for?

Experience. Google added it in 2022 to reward content written by people who have actually done the thing, not just read about it. First-person accounts of doing surgery, running a restaurant, or launching a product now count as a distinct signal.

Is E-E-A-T an algorithm or a set of guidelines?

Guidelines. E-E-A-T lives inside Google's Search Quality Rater Guidelines, which train models but do not directly score pages. That said, the signals it describes (author identity, source citations, corrections policy) all map to algorithmic proxies.

Do I need author schema on every page?

Every editorial page, yes. Product pages and marketing pages should attribute to the Organization. Author schema on every article with a real Person entity backed by sameAs is the ticket.

Does a corrections policy actually help?

Yes. A visible corrections policy signals editorial maturity. It is also the difference between a publisher and a content farm in Google's eye. I ship one on every content-heavy site.

What is the biggest E-E-A-T mistake?

Ghostwriting under fake author identities. AI-generated bios with stock photos. Google detects this and demotes the site. Real authors with real bios and real sameAs coverage are non-negotiable.

How does E-E-A-T interact with AI answer engines?

AI answer engines prefer sources with clean authorial signals. A named expert cited on the topic gets picked over an anonymous article. E-E-A-T doubles as an AEO signal.

Related reading

• Knowledge graph optimization (KGO): entity work that verifies author identity.
• Voice search optimization (VSO): trust signals gate assistant answers.
• Feed and Discover optimization (FEO): publisher signals feed Discover.
• Video search optimization (VDO): creator authority parallels author authority.
• International optimization (GLOBO): E-E-A-T signals per market and language.