E-E-A-T explained: experience, expertise, authority and trust

E-E-A-T comes from Google's Search Quality Rater Guidelines, the handbook for the people Google pays to assess search results. Raters look at real queries and results and judge page quality and how well results meet the need behind the search. Their ratings don't change the ranking of the pages they review. Google uses them to check whether changes to its ranking systems are producing better results.

The guidelines first used E-A-T (expertise, authoritativeness, trustworthiness). In December 2022 Google added a second E for experience, recognising that someone who has actually used a product or been through a situation often has more useful things to say than someone with formal credentials.

You'll see it written as E-E-A-T, EEAT or "double-E-A-T". They all mean the same thing.

Within the four, the guidelines put trust at the centre and call it the most important. Experience, expertise and authoritativeness feed into trust. A page can be written by an expert and still be untrustworthy if it is misleading, out of date or hiding who is behind it.

The bar also depends on the topic. For "Your Money or Your Life" (YMYL) subjects, such as health, finance, legal matters and safety, raters are told to expect a much higher level of all four, because bad information can do real harm.

Not directly. There is no E-E-A-T score in Google's systems and no single signal you can switch on. Google has said its ranking systems use a mix of factors that help identify content showing good E-E-A-T, and that these signals carry more weight for YMYL queries.

In practice that means two things.

First, you can't add E-E-A-T with a plugin. An author box or a schema snippet doesn't create expertise. They help Google and readers see expertise and trust that are already there.

Second, it mostly shows up through things Google can observe: whether other reputable sites mention and link to you, whether your content is accurate and specific, whether readers can tell who is responsible for it, and whether the site behaves honestly.

Google's guidance on helpful content suggests asking three questions of every page:

• Who created it? Make that obvious, with a byline and background where people would expect it.
• How was it created? If you tested something, say how. If automation or AI helped, be open about it where readers would reasonably want to know.
• Why was it created? Primarily to help people, not primarily to attract search traffic.

Those questions are a more useful working test than the acronym itself.

The strongest evidence of experience is detail nobody could write without doing the thing.

• Original material. Your own screenshots, photos, measurements and data rather than stock images and figures copied from elsewhere.
• Specifics. What you tried, what happened, what went wrong, what it cost, how long it took.
• Limits. Where a method doesn't work, or where your product isn't the right choice. Honest caveats read as experience; blanket praise doesn't.

Expertise shows through accuracy and care:

• Claims that are correct and current, with sources linked where a reader might want to check.
• Dates that mean something: a published date, and an updated date only when the content genuinely changed.
• For YMYL topics, an author or reviewer with relevant qualifications, named on the page.

AI-assisted writing isn't ruled out. Google has said it cares about whether content is helpful, not how it was produced. But since March 2024 its spam policies cover scaled content abuse: publishing large amounts of unoriginal content mainly to rank, however it is made. A post that restates the top ten results adds no experience at all. One built on your own product data, screenshots or customer questions does.

Authorship is where E-E-A-T becomes concrete and machine-readable.

Bylines. Put a real name on articles, linked to an author page. "Admin" or "The team" tells readers and machines nothing.

Author pages. Give each author a page with a short bio, their role, relevant experience or qualifications, links to profiles elsewhere (LinkedIn, GitHub, a professional register), and a list of what they have written. Keep it factual.

Person schema. Mark up the author in your Article or BlogPosting structured data as a Person, with name, url pointing to the author page, and sameAs links to their other profiles:

{
  "@context": "https://schema.org",
  "@type": "BlogPosting",
  "headline": "How to chase a late invoice",
  "datePublished": "2026-09-01",
  "author": {
    "@type": "Person",
    "name": "Sam Taylor",
    "url": "https://acme.example/authors/sam-taylor",
    "jobTitle": "Head of Finance",
    "sameAs": ["https://www.linkedin.com/in/sam-taylor-example"]
  }
}

Schema doesn't create authority. It connects the facts you already show, so search engines and AI systems can tie an article to a real person and that person to their other work. That connection matters more as answer engines decide which sources to cite. Our schema markup guide covers the rest of the setup.

Much of E-E-A-T is judged across a whole site, not page by page.

Trust signals

• An About page that says who runs the site or company, with a real registered name and location.
• Clear contact details and a way to reach a person.
• Transparent pricing, refund and cancellation terms for anything you sell.
• A privacy policy, HTTPS everywhere, and no deceptive ads or dark patterns.
• Corrections: fix errors openly and update the date only when content changes.

Authority signals

Authority is mostly what others say about you. Mentions and links from respected sites in your field, genuine reviews on third-party platforms, citations in industry roundups and talks all count. You can't write these yourself, but you can earn them with original data, useful tools and honest comparisons.

Add Organization schema on your homepage with your legal name, logo and sameAs links to official profiles, so your brand is identified consistently.

OpenHelm's GEO audit checks authorship signals, headings and schema alongside other items, and its blog publisher writes posts against a keyword plan and checks them for AI tells before publishing, into the product's own repository. See autoblogging and SEO for how that works.