E-E-A-T for AI Content: What Google Checks and What It Can't

E-E-A-T for AI content is not a score and not an AI detector. Google says plainly that "appropriate use of AI or automation is not against our guidelines" (Google Search Central, 2023). What it rewards is experience, expertise, authority and trust — however the draft was produced. This post shows which of those four a machine can actually check, using the real rules inside Quillly's own SEO scorer, and which ones only you can add.

TL;DR

• Google judges the page, not the tool that wrote it. Scaled, unoriginal pages are the problem, "no matter how it's created" (spam policies).

• An automated scorer can check about half of E-E-A-T: sources, numbers, dates, author schema. It can't check whether you actually did the thing.

• Our own data proves the gap: about 220 live posts averaging a score of 87, yet only 18% are indexed on Bing.

• The fix is a 20-minute human pass per post, covered in the checklist below.

What E-E-A-T for AI content actually means#

E-E-A-T stands for Experience, Expertise, Authoritativeness and Trustworthiness. Google added the first E in December 2022, when its rater guidelines began asking whether content was made by "someone who has first-hand, life experience on the topic" (Google, 2022). The same post notes the guidelines "do not influence rankings directly" — raters grade Google's systems, not your page.

For AI-assisted writing, that means one thing. The question isn't "was this written by AI?" but "does this page carry anything only a real person could know?" An AI can write about a tool. It can't write from having used it, unless you give it that material.

Google penalizes scaled, unoriginal pages, not AI#

Google's spam policy defines scaled content abuse as "many pages generated for the primary purpose of manipulating search rankings and not helping users" and adds that this applies "no matter how it's created" (Google spam policies). Handwritten content farms are covered too. A carefully edited AI post isn't.

Three patterns put an AI blog on the wrong side of that line:

• Scaled paraphrase. Feeding competitor articles to a model and shipping the rewrites. Nothing on the page is new.

• Faceless bylines. No author Google can connect to a real person. Leadgen Economy's analysis argues that AI Overviews increasingly favour pages with a verifiable author entity.

• No first-hand material. Generic "best practices" with no named tool, no result, no date. That's exactly the gap the extra E was added to catch.

"A personalized editorial and optimization workflow is required to ensure quality, originality, and expertise by integrating unique brand insights and first-party data."

What an automated scorer can check (we opened ours up)#

We read the rule file behind the "E-E-A-T Signals" category in Quillly's own scorer. Here's the complete list of what it checks on every draft:

A separate External Sources check wants 3+ outbound links on a post over 1,500 words, spread across more than one domain. On the published page, Quillly emits Article JSON-LD with dateModified, and a Person author with sameAs links when the author has a profile set up.

Notice what's not on that list: whether the first-person claim is true. A regex can see "we tested". It can't see whether you did. We found this post was itself guilty of that: an earlier version quoted client version numbers and a before-and-after audit we can no longer trace to a real session. We removed them in this revision.

Our own data: a high score didn't earn trust#

Here's the number that changed how we think about this. On 28 September 2026, quillly.com had about 220 live blog posts with an average SEO score of 87 out of 100 — above our own publish gate of 85. Yet the search engines tell a different story about how many they chose to index.

Our read: the scorer measures the parts of quality a machine can see. Search engines weigh things it can't, including whether the site is known elsewhere and whether each page adds something new. A 90+ score is the floor for publishing, not proof of E-E-A-T. That's why we now cap new posts at one a week and spend the rest of the time improving or merging existing ones.

Signal 1 — Give Google a real author it can verify#

Google's Article structured data docs say it can use both url and sameAs to disambiguate authors. So the mechanical core is a Person entity with links to profiles that already exist elsewhere.

You need three things: an author page on your domain, Person markup, and a sameAs list of external profiles such as LinkedIn, GitHub, X or a Wikidata item. A schema markup generator writes valid JSON-LD if you'd rather not hand-code it. Here's the shape, with your own values in place of the angle brackets:

{
  "@context": "https://schema.org",
  "@type": "Person",
  "name": "<Your Name>",
  "url": "<your author page URL>",
  "jobTitle": "Founder",
  "sameAs": [
    "<your LinkedIn profile URL>",
    "<your GitHub profile URL>",
    "<your X profile URL>"
  ]
}

On a Quillly-served blog you don't paste this: fill in the author profile (title, bio, avatar, links) and each post's Article markup carries that Person as its author. Three rules still matter:

• Same name everywhere. "J. Doe" on one post and "Jane Doe" on another reads as two people.

• One primary author per post. Credit helpers in the body.

• Validate it. Run the page through Google's Rich Results Test or the Schema.org validator.

For the rest of the schema stack, see the blog schema markup guide.

Signal 2 — Add experience the model couldn't have written#

This is the signal the extra E exists for, and the one no scorer can confirm. What it looks like on the page:

• A named tool and what happened. "Bing's URL inspection showed the page as discovered but not indexed," not "search engines may be slow."

• A result with a number and a date. Like the indexing chart above: our site, our counts, a stated day.

• A failure. "We cut this claim because we couldn't source it" builds more trust than another win.

• A decision rule. "Merge a page when a stronger one answers the same question; unpublish it when none does."

The workflow that works: let your AI draft, then add one "what we actually saw" paragraph under two or three H2s. If a competitor could have written the paragraph without using the product, it doesn't count as experience.

Signal 3 — Link the primary source for every number#

Link the claim to whoever measured it, right where it appears, not to a roundup that repeats it. Google's documentation, a vendor's changelog, a study's own page. When the source is gone, drop the number.

We learned this on this very post. An earlier version quoted a "March 2026 core update" traffic figure from an SE Ranking article. When our link checker flagged that URL as a 404, we couldn't find the figure anywhere else, so it's gone. An unsourced statistic is worse than no statistic: it's the one claim a reader can catch you on.

A practical floor for a 1,500+ word post:

• 3+ external links across at least two domains (the scorer's own threshold).

• One named expert, quoted and linked to their original words where possible.

• One number that only you have, such as counts from your own analytics, dashboard or logs.

For how AI answer engines pick which pages to cite, see our answer engine optimization playbook.

Signal 4 — Keep the page true as facts change#

Trust decays when prices, limits and screenshots drift. On a Quillly-served page, dateModified in the Article markup follows the post's last update, so the signal only helps if the update was a real one.

Three habits keep it honest:

1. Re-check dated facts on a schedule. Prices, plan limits, feature names and third-party stats.

2. Re-run your link checker. Our recurring health check is what caught the dead SE Ranking link above.

3. Improve before you add. If much of your site isn't indexed, a better existing page beats a new one. The content decay guide covers how to pick which pages to refresh first.

The E-E-A-T checklist for your next AI draft#

Run this before every publish. The first block is what software can catch; the second is the part you can't hand off.

Machine-checkable

• 3+ external links across 2+ domains

• At least one statistic, each linked to its source

• A named author with a Person profile and sameAs links

• Article markup with dateModified

• SEO score of 85+

Human-only

• Every linked source actually says what the sentence claims

• Two or three paragraphs describe something you did or measured

• Any number you can't source is deleted, not softened

• The page answers something the current top results don't

• Prices, limits and feature names re-checked today

Quillly's check_blog_seo covers the first block in one call, and get_blog_seo_patches returns the fixes ranked by points. The second block takes about 20 minutes per post, and it's where E-E-A-T actually gets earned. If your posts are stuck despite good scores, work through why AI blogs don't rank.

What to do next#

Pick your three most-visited AI-written posts. For each one, check every statistic against its source, add one paragraph of first-hand detail, and make sure a real author profile is attached. Then re-run the SEO check. That's one afternoon, and it does more for E-E-A-T than a month of new posts.

Want your AI to draft, score and publish with the author markup and source checks built in? Connect Quillly to Claude, ChatGPT or Cursor and run the checklist on your next post.

Keep Reading#

• How AI search chooses citations: E-E-A-T from the citation engine's side.

• Scaled content abuse: the policy killing AI blogs: what happens when experience goes missing at volume.

• Do AI content detectors actually work?: why a detection score isn't an E-E-A-T proxy.

• Blog SEO score: how it's calculated: every category in the scorer, explained.