glossary · e-e-a-t
What E-E-A-T means, and how a page demonstrates it
Four letters from Google's rater guidelines that describe what a trustworthy page looks like. What each one means in practice, what it is not, and how to show it rather than assert it.
E-E-A-T stands for Experience, Expertise, Authoritativeness and Trustworthiness — the four qualities Google's Search Quality Rater Guidelines ask human raters to weigh when judging how far a page and its creator can be relied on.
reviewed 2026-09-02 · by the IT Master editorial team · how we check facts
What E-E-A-T is in practice
E-E-A-T comes from Google's Search Quality Rater Guidelines, the manual given to the people who grade sample search results. Their grades do not attach to individual URLs; they tell Google whether a change to its systems made results better. So E-E-A-T describes what the systems aim at. It is not a dial anyone can turn.
The four parts do different jobs:
- Experience — first-hand contact with the subject. You fitted the camera, ran the migration, handled the return.
- Expertise — knowledge of the subject, earned by qualification or by doing the work for years.
- Authoritativeness — whether others in the field treat you as a source worth citing.
- Trust — the centre of the model. The other three exist to support it.
On a page this looks unglamorous. A named author with a real background. A company identity and a contact route. Dated claims, cited sources, specifications that match what you actually ship, and the details only someone who does the job would think to include. None of it is a plugin setting.
E-E-A-T is not a score in the algorithm. It is the language Google uses to describe what its ranking systems are trying to reward, written down so human raters can judge whether they managed it.
Why it carries more weight in 2026
Two things changed. Fluent prose stopped being scarce, and a second audience arrived.
Fluency used to be a rough proxy for effort. It is not any more, so the signals that separate a useful page from a plausible one are the ones no generator can invent: a measurement someone took, a part number, the failure mode that only shows up on site, an author with something to lose. That is also the practical test behind Google's helpful content guidance — was this written for a reader or for a search engine.
The second audience is the assistants. AI Overviews, ChatGPT and Perplexity answer by selecting passages and attributing them. Selection favours pages that state a claim plainly, date it, and sit on a site that looks accountable — much the same properties raters are asked to look for. Work done for one audience increasingly serves the other.
Trust also has a floor. Where a page affects money, health or safety, thin sourcing is not a small deduction, it is the whole verdict. If your topic touches those, treat author identity and sourcing as part of the build, not a later polish.
How this engine handles E-E-A-T
An E-E-A-T critique is one of the four checks every draft faces before it can publish, and no check is run by the model that wrote the draft. The fact-check and novelty judges come from a different vendor altogether: Claude models write, Gemini and GPT judge, so nothing marks its own homework. A draft can be sent back up to three times with specific fixes; if it still fails, it is not published, and a draft that fails is not charged at the full rate. The mechanism is described under cross-model validation.
Two honest limits. A judge model estimates E-E-A-T, it does not confer it, so passing our check is evidence of care and not a ranking promise. And no engine can manufacture experience a business does not have. What it can do is surface the experience that already exists and has never been written down: first-party data such as product specifications, supplier datasheets and the support questions customers actually ask.
Accountability is named rather than implied: Rodd Azad is Editor-in-Chief, and the standard the drafts are held to is his. Measured, not promised: across 186 Standard-tier runs, 51% of drafts passed every check first time; the rest were revised or rejected.
Five common misreadings
"It is a ranking factor, so there must be a score." There is no E-E-A-T number on a URL. Chasing one leads to cosmetic fixes; supplying evidence a rater could verify does not.
"An author box covers it." A byline with a stock photo and two lines of biography is a label, not a credential. What counts is a person whose stated background explains why they can make the claims on that page, and who is findable elsewhere saying the same sort of thing.
"You need formal qualifications." The extra E was added in December 2022 precisely because that is not always true. Twenty years of installs, a shelf of returns data or a support inbox is experience, and often more useful to a reader than a certificate.
"AI-written pages cannot have it." The page is judged, not the software that typed it. Generic output fails on evidence rather than prose — no first-hand detail, no sources, no accountable publisher — and each of those is fixable. See AI content detection for what actually gets flagged.
"It is a per-page property." Trust accumulates across a site. One strong page on a thin domain is judged in that context, which is why topical authority and E-E-A-T tend to move together.
questions people ask
Is E-E-A-T a ranking factor?
Not directly. There is no E-E-A-T score attached to a URL. The term lives in the guidelines Google gives its human quality raters, and those raters grade sample results to say whether a change to Google's systems improved them. The systems themselves use many signals that correlate with what raters reward. The practical difference matters: you cannot optimise a number, but you can supply the evidence a rater, or a model reading your page, would need in order to call it trustworthy.
What counts as Experience if I sell products rather than test them?
Selling is experience, as long as you write from it. A retailer knows which model comes back most often, which specification confuses buyers, what installers ask before they order, and which two parts are incompatible in the real world. None of that appears in the manufacturer's datasheet and none of it can be lifted from a competitor. Put the return reasons, the support questions and the fitting notes into the page and it carries genuine first-hand experience, even though nobody reviewed the product in a lab.
Can AI-written articles demonstrate E-E-A-T?
The page is judged, not the tool that drafted it. Generic AI output usually fails on evidence rather than style: no first-hand detail, no sources, no dates, no publisher who answers for it. Every one of those is fixable. Ground the draft in data only you hold, cite what you assert, date claims that move, name a real editor, and have something other than the writer check the facts before it ships. An article that clears all five reads the same to a rater whoever drafted it.
What can I fix this week?
Start with identity: a real About page, a postal address, a company number where you have one, and author profiles that say what the person has actually done. Then the pages themselves. Add a reviewed date, cite the source for every figure, replace vague ranges with the specification you can verify, and delete the claims you cannot support. Finally, look off-site, because reviews, directory listings and mentions feed the same judgement. There is a free site check at /check if you want a second pass over a domain before you start.
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