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Testing five visual ideas for an ad used to depend on a designer and a budget. Now an image or a short video takes minutes, and the number of variants has jumped. But more variants does not mean better results: test ten random pictures and you learn which won, not why.
Hypothesis first, generation second
A workable scheme is built on a matrix: what exactly you change and what stays constant.
| Variable | Examples |
|---|---|
| Scene | product close-up / product in use / outcome |
| Emotion | calm / urgency / humor |
| Offer | discount / free delivery / guarantee |
| Format | square / vertical 9:16 / wide |
From the matrix pick 3-5 combinations where one variable changes, and generate visuals only for those. Then the A/B test answers a question instead of just naming a winner.
What models can do today
- Images. Good at backgrounds, atmosphere and object scenes. The weak spots are text in the image (especially Cyrillic), hands, small packaging details and logos. A practical rule: ask for an image without lettering and add text in an editor.
- Video. Short clips from text or an image have become usable for testing, but long scenes and keeping one character consistent between shots remain unstable. For product ads it is often better to animate a finished photo.
- Brand consistency. To keep visuals from drifting apart in style, use references (palette, composition examples), a fixed style description in every prompt and a shared negative list (“no text, no logos”).
Ad platforms built in AI too
The ad dashboards of large platforms now offer generation of backgrounds and text variants themselves. That is convenient, but check which rights and limitations apply in each dashboard and compare with the output of external models. For small teams a third-party tool such as AiHere is useful when you want to compare several models on one idea.
What to check before launch
- Rights and brand. Whether the image reproduces recognizable people, other companies’ logos or a specific artist’s style.
- Labeling. Regulation is moving toward mandatory disclosure of synthetic content: the EU AI Act introduces such duties, and platforms have added labels of their own. Rules depend on country and channel, so check current requirements before launch.
- Truthfulness. You cannot show in an ad a result the product does not deliver; a generated “before/after” easily becomes a violation.
How to read results
A test must collect enough impressions and conversions; an early leader is often noise. Compare on a metric chosen in advance (cost per target action, not clicks). Record which hypothesis won and use that knowledge in the next matrix.
AI speeds up producing variants but does not cancel analytics. The winner is whoever learns faster from tests, not whoever has more pictures.
FAQ
Can AI creatives be used in real campaigns?
Yes, especially for early testing, visual concepts, covers, banners, and short videos. Final assets still need brand review and platform compliance checks.
What matters more: the prompt or the offer?
The offer matters more. A strong visual can attract attention, but campaign performance depends on the audience, value proposition, landing page, and timing.
Should AI add text to ad images?
In most cases, generate the image without text and add copy later in a design editor. This gives better control and avoids broken lettering.
