E-E-A-T for AI Search: What Google and LLMs Actually Look For

The Two Es: Experience vs. Expertise

The first E, Experience, was added to the framework precisely because AI made expertise cheap to fake. Anyone can summarise a topic; far fewer can describe having actually done it. Experience means first-hand, lived knowledge - a reviewer who used the product, a doctor who treated the condition, an agency that ran the campaign. Expertise, the second E, is formal or demonstrated knowledge of a subject. For most commercial queries, both matter, but experience is now the harder signal to counterfeit and therefore the more valuable one to demonstrate explicitly on the page.

Authoritativeness and Trustworthiness: The Reputation Layer

Authoritativeness is about reputation beyond your own website - are you cited, linked, mentioned, and recommended by other credible sources in your field? Trustworthiness, which Google calls the most important member of the family, is about accuracy, honesty, transparency and safety. A page can be written by an expert and still fail on trust if it hides who is behind it, makes unsupported claims, or exists mainly to manipulate the reader. For AI systems synthesising answers, trust signals reduce the risk of amplifying misinformation, which is exactly why they weight them so heavily.

• A named author with a real photo, detailed bio, credentials, and links to their profiles or published work.

• First-hand experience shown through specifics - numbers, screenshots, and a clear account of what you actually did.

• Outbound citations to primary sources, studies, and authoritative references that back up your claims.

• A visible publication or last-updated date, plus transparent contact and organisation details.

Building E-E-A-T Deliberately: A Practical Roadmap

You cannot buy E-E-A-T, but you can engineer the conditions for it. Start by giving every author a real, detailed bio with credentials and links to their profiles and published work. Add first-hand experience to your best commercial pages - specific numbers, screenshots, and what you actually did. Cite primary sources and link out generously to authoritative references. Keep information current and stamp it with a visible last-updated date. Earn mentions from reputable industry sources through genuinely useful work. Over months, these compounding signals move you from a page an AI might quote to the page it quotes by default.

• LLMs reward self-contained, well-attributed passages they can safely lift into an answer without added risk.

• They favour sources with consistent identity across the web - matching name, logo, and details everywhere.

• They down-weight pages that make strong claims with no sourcing, since unsupported claims are risky to repeat.

• They value freshness and clarity, preferring recently updated, unambiguous content over dense, hedged prose.

How do LLMs like ChatGPT and Perplexity use E-E-A-T? Large language models weigh trust and authority signals when choosing which sources to cite, because citing unreliable content risks spreading misinformation. Clear authorship, credible outbound citations, consistent brand identity, and factual accuracy all raise the likelihood that an LLM treats your page as a trustworthy source.

Which pillar of E-E-A-T matters most? Google has stated that Trust is the most important member of the family. Experience, Expertise and Authoritativeness all feed into Trust. A page that is expert and authoritative but not trustworthy - inaccurate, opaque, or manipulative - will still be judged low quality.