**AI engines, like cautious editors, prefer to cite sources they can trust — and clear authorship is the most actionable trust signal.** This check looks at whether your page declares its author with structured data and a visible byline, ideally with credentials. E-E-A-T — Experience, Expertise, Authoritativeness and Trust — is how engines judge whether to rely on content, and anonymous pages compete from behind.
It looks for clear signals of who is behind the content and why they're qualified. Specifically:
- Author schema — an author in Article schema linking to a Person entity (ideally with their own sameAs profiles).
- A visible byline — a named author shown on the page, not anonymous content.
- Credentials / bio — a short bio or credentials establishing the author's expertise.
Clear attribution with schema plus a visible byline passes; a byline only, with no schema or credentials, is a warning; no authorship signal at all is a fail.
GEObubbly looks for author schema or meta, a visible byline, and any credentials or bio that establish expertise. It's a core GEO / LLM Readiness check worth 2 points, because authorship is the most actionable piece of E-E-A-T — the trust framework both search and AI engines use to decide which sources to rely on.
E-E-A-T — Experience, Expertise, Authoritativeness and Trust — is how engines judge whether to trust content, and it's especially decisive for sensitive, commercial and advisory topics. Clear authorship is the most actionable piece: a named author with a visible byline, ideally with credentials or a bio establishing why they're qualified. Reinforce it with structured data — an author in Article schema linking to a Person entity, with that person's own sameAs profiles — which connects the content to a real, identifiable expert that engines can resolve and trust. Anonymous content competes from behind: AI engines, like cautious editors, prefer to cite sources with visible, credible authorship over a page with no one standing behind it. This pairs with cited sources and clear entities to build the trust layer of GEO / LLM Readiness — the signals that decide whether an engine considers you a credible source worth quoting.
E-E-A-T stands for Experience, Expertise, Authoritativeness and Trust — the framework engines use to judge whether content is reliable, especially on sensitive, commercial or advisory topics. It matters for AI because answer engines, like careful editors, prefer to cite sources they can trust, and E-E-A-T signals tell them whether a page is backed by real expertise and credibility. Clear authorship, credentials, accurate sourcing and a trustworthy site all contribute. Pages that demonstrate E-E-A-T are more likely to be both ranked and cited.
Add an author property to your Article (or BlogPosting) JSON-LD that points to a Person object with the author's name, and ideally a url to their bio and sameAs links to their verified profiles (LinkedIn, professional pages). Pair this structured data with a visible byline on the page and a short author bio establishing their expertise. Together, the schema and the visible attribution connect the content to a real, identifiable person engines can resolve and trust, which strengthens the authorship pillar of E-E-A-T.
It can, but it competes from behind. Without a named, credible author, a page gives engines less reason to trust it, which matters more the more sensitive or advisory the topic. AI answer engines, wary of citing unreliable sources, tend to prefer content with visible, credentialed authorship over anonymous pages. Adding a real author with a byline, a bio and author schema is one of the most actionable ways to improve trust signals — so even if anonymous content sometimes performs, attributing your content clearly removes a needless disadvantage.
E-E-A-T is the updated version of the older E-A-T framework, with an extra "E" for Experience added at the front. The original E-A-T covered Expertise, Authoritativeness and Trust; the added Experience pillar recognises first-hand, lived experience with a topic — for example a reviewer who has actually used the product. For content creators it means demonstrating not just credentials and authority but real, direct experience where relevant. Both search and AI engines value content that shows genuine experience alongside expertise.
Make the expertise visible and verifiable. Attribute content to a named author with a short bio and relevant credentials, link that author to their professional profiles via sameAs, and back claims with specific data and citations to credible sources. Demonstrate first-hand experience where it applies, and keep the content accurate and current. The goal is that both a human and an engine can quickly see who wrote the page, why they're qualified, and that the claims are well-grounded — which is exactly what makes a source trustworthy enough to cite.