E-E-A-T — Experience, Expertise, Authoritativeness, Trustworthiness — is the quality evaluation framework that Google has used since 2018 to rate pages through its human quality raters. In 2026, this framework has gained an additional dimension: AI engines (ChatGPT Search, Perplexity, Google AI Overviews) use nearly identical signals to decide which sources to cite in their answers.
The good news: E-E-A-T signals are buildable. This article gives you 8 concrete actions to lay the foundations — and explains why they impact both your Google rankings and your visibility in generative engines.
The 4 dimensions of E-E-A-T
Experience
Added in 2022, this dimension values direct, first-hand experience. An article on real estate investing written by someone who has actually bought properties carries more weight than a generic overview. Concrete signals: first-party data (your own tests, your own numbers), mentions of real cases, personal photos or screenshots, visible publication and update dates.
GEO signal: ChatGPT and Perplexity prioritise sources with proprietary data. An article citing its own experiences with precise numbers is 2× more likely to be extracted than an article paraphrasing other sources.
Expertise
Technical mastery of the subject. No surface-level content, no approximations. Signals: depth of treatment, correct technical vocabulary, cited sources (studies, RFCs, official case studies), author pages with verifiable credentials.
Common mistake: confusing length with expertise. A 5,000-word article full of generalities has less expertise signal than a 1,500-word article with three precise data tables.
Authoritativeness
How other sources perceive your expertise. Main signals: backlinks from recognised sites in your sector, mentions in trade press articles or sector guides, author profile cited or interviewed by others, presence in AI engine "sources" on queries related to your niche.
GEO signal: 40% of sources cited in Google AI Overviews are not in the organic top 3 — but they are consistently well-cited within their niche. Topical authority matters more than global DR.
Trustworthiness
Transparency and honesty. Complete legal pages (terms, privacy policy, contact), contact page with a real address and email, identifiable author, documented error corrections, no misleading content.
Negative signals that damage Trustworthiness: no legal pages, no "About" page, missing or truncated publication dates, multiple broken links.
Why E-E-A-T also governs GEO
LLM training crawlers (GPTBot, PerplexityBot, ClaudeBot, CCBot) prioritise sources using criteria close to E-E-A-T:
| Signal | SEO impact (Google) | GEO impact (ChatGPT/Perplexity) |
|---|---|---|
| Author page + sameAs | ✅ YMYL ranking | ✅ Citation probability +23% |
| Organization schema sameAs | ✅ Knowledge Graph | ✅ Entity recognised by LLMs |
| Sectoral backlinks | ✅ PageRank | ✅ Topical authority crawled |
| Proprietary data | ✅ Freshness signal | ✅ Preferred source for precise facts |
| Complete legal pages | ✅ Trustworthiness | ✅ LLM safety criterion |
This table reveals a useful truth: E-E-A-T signals are in near-total intersection with GEO citability signals. You don't have to choose — the optimisations reinforce each other.
If you haven't laid the GEO foundations yet (llms.txt, structured data, crawlable content structure), start with our GEO guide before going further.
8 concrete actions to build your E-E-A-T signals
1. Create complete author pages with sameAs links
Every author publishing on your site needs a dedicated page with: real bio (not aspirational), photo, contact email or profile, and sameAs links to verifiable public profiles (LinkedIn, GitHub, Twitter/X, Google Scholar if applicable).
In your Article JSON-LD, the author.sameAs field is the most underused — and the most impactful for GEO:
{
"@type": "Person",
"name": "First Last",
"sameAs": [
"https://linkedin.com/in/your-profile",
"https://twitter.com/your-handle"
]
}
2. Complete your "About" page and legal pages
An "About" page with team history, real mission, and credentials (training, years of experience, publications) is a strong Trustworthiness signal. AI engines explicitly index it to validate the source's identity.
Required pages: About, Contact with physical address, Legal notice, Privacy policy. A missing page is a strong negative signal on YMYL topics (Your Money or Your Life).
3. Build external citation (not just backlinks)
The distinction matters. A backlink on an orphaned page of a low-authority site is worth little. A mention in a sectoral newsletter article, a podcast transcript, or a study published by a recognised player is worth a lot — even without a hyperlink.
Actionable tactic: write original data points (a study, a benchmark, a calculation nobody has done), then cite them in your own newsletter. Sources that repeat that number credit you, and that credit is crawled by AI engines.
4. Set your Organization schema with extended sameAs
On your homepage (once, statically):
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "Your site",
"url": "https://your-site.com",
"logo": "https://your-site.com/logo.png",
"sameAs": [
"https://linkedin.com/company/your-company",
"https://twitter.com/your-handle",
"https://github.com/your-org"
]
}
The sameAs on Organization is the most direct entity-based signal for ChatGPT and Perplexity: it tells them that your domain and social profiles are the same entity.
5. Include measurable proprietary data
"Based on our analysis of 10,000 pages" > "According to studies, it is generally accepted that…". Data only you possess can't be copied and forces citation.
For a SaaS or tool: regularly publish aggregated usage stats (anonymised). For an editorial blog: run your own surveys, measure, compare. For an e-commerce: publish insights on your sales data.
6. Make update dates visible and honest
datePublished and dateModified in your JSON-LD must be synced with what's displayed in the page. "Updated June 8, 2026" visible in the HTML + the same value in dateModified = consistent freshness signal.
Don't falsely date articles to a recent date without a real update — Google detects it through previous crawl caches. AI engines treat content with inconsistent dateModified as less trustworthy.
7. Show depth of expertise in your H2/H3s and section introductions
An easy expertise signal to implement: start each main section with 1-2 sentences showing you know what you're talking about before explaining. Not "here's how X works" — but "X works on principle Y, which most tutorials simplify incorrectly because…".
This pattern is caught by LLM training crawlers as a sign of above-average expertise.
8. Get reviews and citations from recognised sources in your niche
Submit guest posts to recognised industry publications. Participate in podcasts with transcripts. Get your content linked from relevant Wikipedia pages (by contributing honestly to articles where you have real expertise).
Each citation in a high-authority source is a topical authority signal that both Google and LLMs credit.
E-E-A-T and YMYL: sectors where the stakes are highest
Google defines YMYL (Your Money or Your Life) as pages that can significantly affect users' health, safety, financial situation, or happiness. These pages are subject to stricter E-E-A-T criteria in the Quality Rater Guidelines.
Typical YMYL sectors: finance/investment, health/medicine, law, nutrition, security. In these sectors, a page without an identifiable author, without verifiable credentials, without cited sources can be kept at very low positions even with a good link profile.
For SaaS founders in these sectors: this is non-negotiable. Author pages, credentials, cited official sources, and complete legal pages are prerequisites, not optimisations.
Key takeaways
- E-E-A-T = Experience + Expertise + Authoritativeness + Trustworthiness, evaluated by Google's human raters and LLM crawlers
- Signals are nearly identical for organic SEO and GEO citability — one optimisation stack for both
- Author pages + sameAs, proprietary data, and Organization schema are the 3 highest-ROI levers
- YMYL sectors (finance, health, law) have non-negotiable E-E-A-T requirements
- Topical authority (being recognised in your niche) matters more than global domain authority for AI engines
Get your free /100 score — SeAudit's audit evaluates your E-E-A-T signals (author pages, Organization schema, Trustworthiness) across its 5 scoring axes. For priority actions ranked by impact, the full report gives you the precise action plan.
FAQ
Is E-E-A-T a direct ranking factor?
Not in the strict sense — Google has no "E-E-A-T score" injected directly into the ranking algorithm. But the signals that build E-E-A-T (links, freshness, schema, author authority) are themselves ranking factors. The distinction matters: you're not trying to "tick E-E-A-T boxes" but to build concrete evidence that your pages demonstrate all 4 dimensions.
Can you build E-E-A-T for a site with no identified human author?
Yes but it's harder. Organization replaces Person in the schema, the "About" page must be very detailed about the editorial team, and external citations become even more important. For YMYL sectors, it's strongly discouraged — the Quality Rater Guidelines insist on author identifiability.
How long does it take to see the effects of E-E-A-T optimisations?
Technical signals (schema, legal pages, authors) can be crawled within 2-4 weeks. The impact on rankings then depends on how frequently your niche is re-evaluated by human raters — 2 to 6 months depending on competitiveness. Effects on GEO citability are measurable faster (1-2 months) on queries where you're already appearing.
Does E-E-A-T apply to category and collection pages, or only to articles?
All indexed pages. Category pages, product pages, service pages are all evaluated. On product pages (e-commerce), the key signals are: authentic customer reviews, precise technical descriptions, identified author/publisher, complete Product schema with AggregateRating.
What is the difference between E-E-A-T and Google Core Updates?
Core Updates are algorithm updates that re-evaluate positions across the entire index. E-E-A-T is the qualitative evaluation framework used by human raters to train the data powering those updates. A Core Update can therefore massively reward or penalise sites based on their implicit E-E-A-T score — which explains why sites with strong authority signals better withstand Core Updates.