LLM-drafted-then-edited vs. human-written-from-experience

LLM-drafted-then-edited vs. human-written-from-experience

The question: for E-E-A-T purposes, where does the line fall between AI-drafted content a human edits, and content a human writes from direct experience?

Google's stated position is that production method is not itself disqualifying — its September 2023 guidance affirmed content is judged on quality, helpfulness, and the E-E-A-T it demonstrates, regardless of whether AI was involved. But that same framing exposes the real constraint: the 'Experience' leg is precisely what an LLM cannot supply. A model has no first-hand use, no original measurement, no lived edge cases.

So the comparison is not 'AI versus human' but 'content with experiential substance versus content without it.' AI-drafted-then-edited is fine for the expository scaffolding where experience is not the value-add. It fails where the whole point is lived testing or original observation, which it can only fabricate.

The efficient division of labor: let the model handle structure and summary of established knowledge; require the human to inject the non-substitutable experiential layer — original data, specific failure modes, unique media.

Caveat: 'AI is fine if quality is high' is the official line, but the helpful-content and 'scaled content abuse' policies target AI used to mass-produce thin pages — method becomes relevant when it enables abuse.

What we still don't know: whether systems can detect the absence of genuine experience in fluent AI prose, or whether they currently rely on cruder proxies like originality and on-page specifics.