The obvious move after a flat page is to feed the generator a better prompt and try again. It almost never fixes conversion. Regenerating reshuffles the same median patterns into a different generic page — you trade one flavor of average for another. You don't escape the average by sampling from it more.
The deeper issue is that the model doesn't have the inputs conversion runs on. It has no access to your real proof, your customers' actual words, or your specific positioning. So it does the only thing it can: it invents (hallucinated testimonials, made-up stats) or it generalizes ("for modern teams"). Both are exactly what you need to remove.
The conversion edge lives in human-supplied inputs: voice-of-customer language pulled from real sales calls and reviews, your genuine outcomes, the specific objections you hear over and over. None of that is in the training data. The right division of labor is clean — let AI handle layout, scaffold, and components, where it's genuinely strong; keep the headline, value prop, proof, and objection copy human and specific.
This is precisely the gap an outside read surfaces fast. Instead of guessing which of the six modes is bleeding the most, a structured audit ranks them for you against a fixed rubric.
If you want to see how that scoring works, here's the 12-dimension methodology behind every audit — the same rubric that separates a clarity problem from a proof problem so you fix the costliest one first.