E-E-A-T for AI-Generated Content: What Google Actually Checks

Google's position, in its own words

Much of what circulates about Google and AI content is inference. Google has published enough to work from its own words instead.

Its February 2023 guidance on AI-generated content sets out the principle. Google says its focus is on the quality of content, "rather than how content is produced," and it draws the line at intent: using automation, including AI, "to generate content with the primary purpose of manipulating ranking in search results is a violation of our spam policies."

So there are two separate questions for any page. Is it helpful, reliable content for people? And was it produced primarily to manipulate rankings? AI can be involved in the answer to either.

What E-E-A-T is, and which part matters most

E-E-A-T stands for experience, expertise, authoritativeness and trustworthiness. It describes the qualities Google wants to reward, and it is part of what Google's human quality raters assess.

Google's guidance on creating helpful content ranks them: "Of these aspects, trust is most important. The others contribute to trust, but content doesn't necessarily have to demonstrate all of them."

That is useful for anyone publishing with AI. The quality that matters most is also the one a content team can most directly show on the page: sourced claims, named authors, a clear account of who is responsible for what is published.

What gets punished: scaled content abuse

Google's spam policies define the problem precisely: "Scaled content abuse is when many pages are generated for the primary purpose of manipulating search rankings and not helping users."

Its examples include:

• "Using generative AI tools or other similar tools to generate many pages without adding value for users"
• "Scraping feeds, search results, or other content to generate many pages (including through automated transformations like synonymizing, translating, or other obfuscation techniques), where little value is provided to users"
• "Stitching or combining content from different web pages without adding value"

Notice what is not on the list. AI assistance is not. Many pages are not, on their own. The combination Google names is volume produced to manipulate rankings without adding value.

What quality raters are told

Google's Search Quality Rater Guidelines are the handbook for the people who evaluate search results. The January 2025 update added a definition of generative AI for the first time, as reported by PPC Land, and the current edition, dated 11 September 2025, keeps it: "Generative AI is a type of machine learning (ML) model that can take what it has learned from the examples it has been provided to create new content, such as text, images, music, and code."

The rating that matters here is the bottom one. Section 4.6.6 says: "The Lowest rating applies if all or almost all of the MC on the page (including text, images, audio, videos, etc) is copied, paraphrased, embedded, auto or AI generated, or reposted from other sources with little to no effort, little to no originality, and little to no added value for visitors to the website." MC is the page's main content.

AI generation is named there, but inside the same test as copying: little to no effort, originality or added value. That is exactly the risk of an AI tool that rewrites what already ranks.

The disclosure questions

Google's helpful content guidance includes questions for creators about how content was produced. Two concern automation directly:

• "Is the use of automation, including AI-generation, self-evident to visitors through disclosures or in other ways?"
• "Are you providing background about how automation or AI-generation was used to create content?"

These are framed as questions to consider rather than rules, but they indicate what Google regards as good practice: being open about how a page was made, when automation substantially made it.

What this means for a team using AI

1. Add something only you have. Your product knowledge, your data, your customers' experience with permission, your judgement. Content that restates what already ranks is the pattern the rater guidelines rate lowest.
2. Source every claim. Trust is the quality Google ranks first, and a sourced claim is the most direct way to show it. For how often AI models state things that are not true, see What the Research Actually Says About AI Hallucinations in Marketing Content.
3. Put real people's names on it. Name the author, link a real profile, and mark the author up with Person schema. Our Article schema generator does the markup.
4. Publish at the pace you can stand behind. The scaled content abuse policy is about volume without value. A calendar your team can review properly beats a backlog published in a week.
5. Be open about automation where it is material. Google's own questions point that way.
6. Keep dates honest. Update the date when the content changes, and not otherwise.

A check before publishing

• Would this page be useful to someone who arrived without searching?
• Does every statistic, study and product claim link to its source?
• Does it contain something that could not have been produced by rewriting the current top results?
• Is a named person accountable for it, with a profile a reader can check?
• If automation did most of the writing, would a reader be able to tell how it was used?

A page that passes those questions is unlikely to be the one Google's policies describe. For why unsourced AI claims appear in the first place, see Why AI Content Hallucinates, and for the approach we build Veritas around, Citation-First AI.