On the AI video ad platform I work on, every scene goes through the same painful loop: write a prompt, send it to an AI video model provider, wait two minutes, open the result, squint at the frame, and decide what went wrong. Camera too wide. Product missing from the hero shot. Color palette drifted warm when the brand brief says cool neutrals. Avatar looks like a different person than scene three.
That loop was manual, slow, and expensive. Each regeneration burns GPU credits. Operators were becoming prompt engineers by accident — and still missing subtle failures until stitch time, when fixing scene four means re-rendering everything downstream.
The insight behind vision-in-the-loop prompt authoring is simple: the model that wrote the prompt can also look at its own output and rewrite the prompt with surgical fixes. Not a full replan — a per-scene correction grounded in the actual generated frame, not the operator's memory of what they hoped would appear.
The manual loop we were trying to kill
Before this work shipped, the swipe iteration flow looked like this:
