SourceCited / E-E-A-T

Strategy · Trust

E-E-A-T: experience, expertise, authority & trust

AI engines cite sources they can attribute to a real, qualified entity. Build E-E-A-T with a named author, Person schema, first-hand experience, and a consistent entity across the web.

Answer first

E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is how Google — and AI answer engines — judge who stands behind a page. It is built from concrete signals: a named author with a real bio and Person schema + sameAs, first-hand experience on the page, cited sources, and a consistent entity across the web. The single biggest fix for most sites is replacing a faceless byline with a real, corroborated author.

On this page
  1. What E-E-A-T is
  2. The signals
  3. Build an author identity
  4. Show real experience

What E-E-A-T is, and why AI leans on it

E-E-A-T — Experience, Expertise, Authoritativeness, Trustworthiness — is Google's framework for judging who is behind a page, and AI answer engines lean on the same signals when deciding whose claims to trust. It is not a single score or a tag you add; it is the accumulated evidence that a real, qualified person or organization stands behind the content. For AI citation it matters twice over: the consensus engine wants a nameable, corroborated entity to attribute a claim to, and a faceless site gives it nothing to hold onto.

The extra E

The first E — Experience — is the newest and most under-used: evidence you have actually done the thing (used the product, run the test, visited the place), not just researched it. First-hand experience is exactly the information gain AI rewards and competitors cannot copy.

The signals, concretely

E-E-A-T is built from specific, checkable things — not vibes:

SignalWhat it looks like
Named author with a real bioA byline that links to an author page listing background and qualifications — not “Admin” or “The Team”
Person schema + sameAsStructured data tying the author to their profiles (LinkedIn, professional pages) so engines resolve them to a real entity
First-hand experience“We tested…”, original screenshots, specific results — evidence you did it, not just read about it
Cited sourcesOutbound links to primary research and data, which raise trust even as they send a link out
Consistent entityThe same name, description, and facts about you across your site and third-party surfaces (see off-page consensus)
Trust basicsHTTPS, a real About and contact, accurate dates, and for commerce, clear policies
Burned

Publishing under “Admin” or a faceless team name. It is the most common E-E-A-T miss and the easiest to fix. An anonymous page gives the consensus engine no entity to attribute or corroborate — name a real author and give them a real page.

Build a real author identity

The highest-leverage fix is turning a faceless site into one with a named, corroborated author. Do it once, centrally, and reuse it: one canonical author entity, a proper bio page, Person schema with sameAs pointing at real profiles, and a visible byline on every content page linking back to it. That is exactly how this site attributes its own work.

prompt · Draft an author identity and Person schema
From these real details about the author [name, genuine background, qualifications, relevant
first-hand experience, and profile URLs like LinkedIn/GitHub]:
1. Write a truthful 60-word and 120-word author bio that states real experience and expertise
   without inflating anything.
2. Generate Person schema (@type Person, with name, url to the author page, jobTitle,
   description, and a sameAs array of the profile URLs).
3. Give me a one-line byline snippet that links to the author page with rel="author".
Keep every claim verifiable — do not invent credentials.

E-E-A-T only helps if it is true. Overstated credentials are a trust risk, not a trust signal.

Show experience competitors can't fake

The durable moat is the first E. Anyone can summarize; only you can show what you actually did. Put the evidence on the page: original screenshots from a tool you really used, specific numbers from a test you ran, before/after results, a photo of the thing in your hands. This doubles as original data and as information gain — one piece of genuine first-hand evidence can lift a page above ten that merely rephrase the consensus.

NW
OSINT researcher and founder; runs a network of ranked content & tools sites
Updated July 21, 2026

Keep reading

[3] Off-page consensus

Corroborate the entity off-site

Publish original data

First-hand evidence as data

Surviving core updates

E-E-A-T is what updates reward

Questions people actually ask

What is E-E-A-T?

Experience, Expertise, Authoritativeness, and Trustworthiness — Google's framework for judging who is behind a page, and the signals AI answer engines lean on when deciding whose claims to trust. It's built from concrete evidence (named authors, credentials, first-hand experience, citations, a consistent entity), not a single tag or score.

Does E-E-A-T affect AI citations?

Indirectly but strongly. AI search is a consensus engine that wants to attribute a claim to a real, corroborated entity; a faceless page gives it nothing to hold onto. Named authors with Person schema, first-hand experience, and a consistent cross-web entity all make you a safer source to cite.

What's the fastest E-E-A-T win?

Replace a faceless byline ("Admin", "The Team") with a real named author: a truthful bio page, Person schema with a sameAs array pointing at real profiles, and a visible byline linking to it on every content page. It's a one-time, central fix that lifts every page.

How is the 'Experience' E different from Expertise?

Expertise is knowing the subject; Experience is having actually done the thing — used the product, run the test, been to the place. First-hand experience is the newest and most under-used signal, and it doubles as information gain because competitors can't copy what you actually did.