A common problem with AI-generated product content is that it often makes weak product data look solved when it isn’t.
I keep seeing this around product cards because it’s one of those use cases in ecommerce that calms people down very fast. You need titles, bullets, descriptions anyway, the model gives you something clean in minutes, and suddenly it feels like progress. But a cleaner card is not the same thing as a more trustworthy one.
Very often I see that if you look a bit lower, you can find the same old problems: attributes don’t fully match, some values are vague, some are missing, and mappings are off. The AI didn’t fix that but gave the mess better phrasing.
That’s why I keep thinking AI-generated product content for catalog gets framed at the wrong layer. From my side, it makes more sense to start earlier. Get the attributes into a shape you can actually trust, validate what matters, stop uncertain cases from flowing straight through, and only then generate from approved data. Less impressive in a demo, obviously. But demos are very tolerant. Production catalogs usually aren’t.
Do you let AI generate from whatever is already in the catalog, or do you force cleanup and validation first?