Why does E-E-A-T matter to AI models?
A generative engine takes responsibility for an answer nobody will verify by clicking a link. So when choosing a source it favours pages that show who is writing, what the claims rest on and when they were last checked. An anonymous text with no sources is a risk the model avoids.
This is the one dimension where improving does not mean rewriting sentences. An author bio with credentials, a citation to a study, an update date, a contact - these elements are either there or they are not.
What will you see in the report?
You get four separate scores together with a list of detected signals - each with the quotation from your content that supports it.
Plus a list of high-risk claims with no source in their paragraph. This is the most common and cheapest fix: a number already in the text only needs a citation.
Sample recommendations
Fragments of a report from an audit of an electronics store category page.
Problem: The content has no author and no trace of experience — to the model it is an anonymous category description.
Category copy with no byline, no bio, no note on how the hardware was tested.
Sign the content with credentials: “Kamil Nowak, gaming peripherals specialist, 8 years of testing esports hardware”.
Problem: The trust signals a transactional page needs are missing: returns, warranty and payment security.
All products come from official distribution and carry full technical support.
Put the specifics up front: “30-day free returns, 24-month manufacturer warranty, SSL-encrypted payments”.
How do we measure E-E-A-T?
First we detect signals algorithmically in the content and the page code, and only then does the model verify them and judge their strength. The rule is strict: every accepted signal must be anchored in a specific fragment of the text - a signal without a quotation is rejected, so the model cannot credit you with expertise the page does not claim.
The four components and the signals behind them
| Component | What raises it |
|---|---|
| Experience | A first-hand account, a case study, photos and screenshots from use, a description of testing you ran yourself |
| Expertise | Citations to research, data with a stated source, industry terminology, an explanation of mechanisms, a bibliography |
| Authoritativeness | An author bio with credentials, institutional affiliation, publications, external citations, awards |
| Trustworthiness | Disclaimers, an update date, author contact, an editorial policy, a secure connection |
We also verify sourcing - signals read from the page code, not from the text alone
| Signal | What is checked |
|---|---|
| Source verifiability | External links, citation markers, references to known institutions and publications |
| Source diversity | The number of distinct domains - leaning on a single source weighs less |
| Author identity | Author profile links in structured data: Wikipedia, ORCID, Google Scholar, LinkedIn |
| Claim corroboration | Whether risky claims - statistics, dates, research findings - have a source in the same paragraph |
What raises and what lowers the score?
Raises
- A bylined author with concrete credentials, not “the editorial team”
- Citations to research and data with the source stated
- Links to a range of institutions instead of repeated links to one site
- A visible update date and a contact
- A description of your own test, deployment or client case
Lowers
- Text with no author and no date
- Statistics given with no source
- Claims of competence with nothing behind them (“years of experience”)
- No external links whatsoever
- Referring exclusively to your own material
Frequently asked questions
Is E-E-A-T a ranking factor?
Not as a single parameter. It is a set of quality criteria Google uses to describe good content, and generative models apply the same reasoning when choosing a source. We measure the signals actually present on the page.
Is adding “author: the editorial team” enough?
No. A signal counts only when it is anchored in the content - a byline with no credentials, affiliation or trace of experience changes nothing. The rule is deliberately strict so the audit does not credit expertise the page never claims.
Which sources count for the most?
Recognised institutions and industry publications, and a variety of them. Five links to five credible domains weigh more than five links to a single site.