While much of r/FacebookAds was complaining about performance, an ecommerce advertiser posted that his accounts were fine and the difference was creative volume. His best performers, he says, are AI generated images that lean hard into a specific customer persona.
How to test AI generated ad images on Meta
The production loop has four steps. Research: ask an AI assistant for image prompts built around your core demographic, download competitors' strongest ads from the ad library, and look at brands outside your niche with similar funnels. Prompting: have the model analyse those references and write five or more prompts per style, for two or three personas if you are unsure who buys. Generation: queue everything in an image model and aim for 20 to 40 images. Testing: put them all live.
The testing structure depends on the pixel. On a fresh account he uses a campaign budget optimised campaign with one ad set per image, a $15 a day minimum per ad set, left alone for seven days. On an account with data he runs an Advantage+ shopping campaign with no targets until it reaches 30 to 50 conversions, then switches to a cost per result target near break even. When an image wins, he feeds it back to the model and asks for more in that style. If nothing works across forty images, he says, the problem is the funnel, not the ads.
Why it works
Meta's delivery system rewards diverse creative, and images are now cheap enough that you can let the platform pick winners instead of guessing.
How to copy it
Budget a week and a few hundred dollars. Write your personas down first. Name ads consistently so you can see which persona and style won, then iterate on that.
Credit: u/GeniusBuffalo on Reddit