Picture a site that passes every technical check: decent backlinks, fast load times, clean schema. On paper it looks fine. A Google quality rater would spot the problem in about 40 seconds: no named authors anywhere, an About page that reads as if written by someone who never visited the company, and a single trust badge in the footer that is years out of date. That is what a failing E-E-A-T audit looks like, and it rarely shows up in a technical crawl.
Done by hand, an audit at that depth takes most of a working day. Using Claude as a structured scoring layer cuts it to well under two hours. Here is exactly how.
What E-E-A-T Actually Is (And What It Isn't)
Quick level-set before we get into the tooling. Google's Search Quality Rater Guidelines define E-E-A-T as Experience, Expertise, Authoritativeness, and Trustworthiness. That fourth "E" for Experience was added in December 2022 and it changed things. It's no longer enough to prove you know something. You need to show you've done something.
This matters differently depending on your vertical. A recipe blog needs to prove the author actually cooked the dish. A fintech comparison site needs to prove someone with financial credentials vetted the numbers. An HVAC contractor site needs a real human who installs boilers, not a marketing agency writing blog posts about boilers.
E-E-A-T is not a ranking factor in the direct algorithmic sense. It's a framework that quality raters use to evaluate pages, and that framework informs how Google trains its systems. The distinction matters because you can't "optimise" for it the way you'd target a keyword. You either have it or you don't.
Why I Brought Claude Into the Process
Honest answer: I was tired of doing the same checklist in Notion for every client. Seahawk gets a mix of inbound audits, anywhere from a local plumber's five-page site to SaaS platforms with 3,000 URLs. The depth varies, but the E-E-A-T questions are the same across every single one.
Claude (I'm using the claude.ai interface, not the API, for most of this) is genuinely good at one specific thing: pattern recognition against a structured rubric when you feed it the right input. It's not crawling your site. It's not replacing Screaming Frog or Ahrefs. What it does is take a page of raw HTML or copied page content and apply a consistent evaluative lens faster than I can.
The place to test this is YMYL content, where the stakes for E-E-A-T are highest. Paste in a homepage, an About page and two blog posts, and ask Claude to score each E-E-A-T dimension from 1 to 10 with specific evidence from the text for each score. The kind of thing it reliably flags: blog posts written in the first person with no byline, founder credentials mentioned once in passing rather than substantiated, and health claims with no primary source linked. A careful reader catches most of that. Not all of it, and not in fifteen minutes.
Building the Prompt Framework
This is where most people go wrong. They treat Claude like a magic button: "audit my site's E-E-A-T" and expect something useful back. You won't get it. You need to constrain the task aggressively.
Here's the basic structure I use:
- Role-set the model. Tell it you're an SEO consultant auditing for Google's E-E-A-T criteria and that it should evaluate as a trained quality rater would.
- Feed it the raw material. I paste in: the full About page text, two to three representative article/page bodies, any visible author bio text, footer content, and any schema markup I've pulled from Google's Rich Results Test.
- Ask for a scored breakdown. Specifically: score each of the four dimensions 1-10, cite the exact text that informed each score, and list the three highest-priority changes.
- Ask a follow-up about what's missing. "What signals a quality rater would typically expect to find on a site in [vertical] are absent from this content?"
That last prompt is the one that earns its keep. Because Claude will tell you things like: "There is no mention of editorial review processes, no author credentials linked to external verification, and no clear distinction between opinion and factual claims in the health content." That's a paragraph I'd write in an audit report anyway. Getting a first draft of it in 30 seconds is useful.
A Note on What You Feed It
Garbage in, garbage out. If you paste in only the homepage, you'll get a surface-level score. I always include:
- The About/Team page (or the absence of one, which I note in my prompt)
- One piece of YMYL content if applicable
- One piece of non-YMYL content for contrast
- The footer (credentials, contact info, trust badges)
- Any visible review snippets or testimonials
The more representative the sample, the more honest the output.
Scoring the Four Dimensions
Let me walk through what Claude is actually evaluating and where I've learned to push back on its assessments.
Experience
This is the hardest one for Claude to score accurately because experience signals are often visual or contextual. A photo of a dentist in her surgery. A video of a builder on a job site. Original photography versus stock. Claude can't see images, so you have to describe them or note their presence explicitly in your prompt. I've started adding a line like: "Images on this page include X stock photos and no original photography" so the model can factor it in.
Expertise
Claude is actually quite good here. It looks for credential language, qualification mentions, professional association references, and whether claims are substantiated with sources. For a law firm site I audited in March, it correctly identified that the solicitors' SRA registration numbers were buried in a PDF rather than visible on the page, which is a missed trust signal most humans would scroll past.
Authoritativeness
This is where Claude will sometimes over-score sites that use authoritative language without having authoritative substance. Confident prose is not the same as cited evidence. I've learned to add a specific instruction: "Distinguish between authoritative tone and authoritative substance. Score only on substance."
Trustworthiness
Arguably the most weighted dimension, according to Google's own guidelines. Claude is good at spotting missing contact information, unclear ownership, absent privacy policies, and thin or generic About pages. Where it struggles is in evaluating the quality of trust signals, not just their presence. A privacy policy copied from a template generator is not the same as one drafted by a solicitor. Claude will credit the presence, not the quality. You have to ask a separate follow-up: "Are any of the trust signals present but likely to be low-quality or templated?"
A Worked Example: Fintech Comparison Pages
Take an illustrative case: a fintech comparison site with strong domain authority and decent content whose rankings have slid for months while technical audits keep finding nothing.
Run the Claude E-E-A-T framework across the top 20 pages by traffic loss, pulled from Google Search Console and sorted by click delta over six months. On a site like this the framework tends to flag the same pattern page after page: financial product recommendations with no disclosed methodology, no named author with financial credentials, and no date of last review. Classic YMYL trust failure.
The fix isn't technical. It is: add a named senior editor with a relevant financial background to the byline, add a "last reviewed" date, and put a one-paragraph methodology disclosure on every comparison page. Then measure recovery over the following quarter against comparable pages you did not touch, because nothing in E-E-A-T work is attributable without a control.
Limitations You Need to Know
I'd be doing you a disservice if I made this sound like a complete solution.
- Claude cannot access your live site. Everything has to be manually fed in. For large sites, this means sampling, which introduces blind spots.
- It can't evaluate backlink profile, which is a significant input into authoritativeness at the domain level. You still need Ahrefs or Semrush for that.
- It doesn't know your vertical's specific trust norms unless you tell it. A medical site and a cooking site have very different baseline expectations. Prompt for it explicitly.
- It will occasionally hallucinate specific recommendations ("add your GMB listing to your footer" for a site that already has it). Always verify outputs against the actual page.
- Claude is not a replacement for actually reading the content. I still read everything. The model gives me a structured starting point, not a finished audit.
One thing I've found genuinely useful: cross-referencing Claude's scores against the Semrush Authority Score and manual checks in Ahrefs Site Explorer. When Claude scores Authoritativeness low but the domain authority is high, that usually means the site has links but thin on-page credibility signals. When it's the reverse, there's often strong content but weak off-page trust. Both scenarios need different fixes.
Integrating This Into a Standard Audit Workflow
Here's how I sequence it now for new client audits:
- Pull traffic data from Google Search Console and identify pages with significant year-on-year click drops.
- Run Screaming Frog to flag missing schema, broken author links, missing contact pages.
- Pull rich results data for any structured data present.
- Feed the top 10 affected pages into Claude using the prompt framework above.
- Consolidate Claude's findings into a scored summary by dimension.
- Cross-reference with backlink profile in Ahrefs.
- Prioritise fixes by impact: Trust signals first (fastest to implement, often highest impact), then Expertise and Experience signals, then Authoritativeness-building (which usually means content strategy and digital PR, so slower).
The whole process for a ten-page sample is about 90 minutes. For a larger site, I'll usually sample 20-30 pages and extrapolate. It's not perfect, but it's honest.
FAQ
Does Claude actually understand Google's quality rater guidelines?
Broadly, yes. It has strong training-data coverage of SEO literature and the guidelines themselves. But it's not interpreting the guidelines in real time against a live algorithm. Think of it as a well-read analyst who's studied the guidelines, not a direct proxy for how Google evaluates pages.
Can I use ChatGPT or Gemini instead?
You can. Claude tends to give more structured, citation-specific outputs when you ask for scored rubrics. ChatGPT goes broader and more conversational. Gemini may bring sharper vertical awareness in some financial contexts, possibly because of Google's own data. Run the same rubric through each on one page and use whichever gives you the most consistent outputs.
How often should I run an E-E-A-T audit?
For any YMYL site, I'd say every six months minimum, or after any major content refresh or algorithm update that affects your rankings. For non-YMYL sites, annually is usually enough unless you see an unexplained traffic drop.
Do I need a paid Claude plan for this?
The free tier has context window limits that make it awkward when you're pasting in multiple full pages. Claude Pro is worth it for this workflow. At £18/month (as of writing), it pays for itself on the first client audit.
What if my client's site is in a non-English language?
For French or German content, Claude handles the rubric but add explicit language to your prompt: "Evaluate this content against E-E-A-T standards; note any language-specific trust conventions that may differ from English-market norms." It won't know every local trust norm (German sites, for instance, have specific Impressum legal requirements) so you need to prompt for local context.
Back to the site from the opening. The fix for that pattern is unglamorous: correct the author attributions, rewrite the About page, have team members each write a short bio with their relevant experience, and add NAP markup properly to the footer. Expect recovery to take weeks, not days. The difference Claude makes is on the audit side: the same issues surface in under an hour instead of a full day.
That's the actual value here. Not replacing judgment. Just getting to it faster.