A catalog with twenty products can be written by hand. A catalog with twenty thousand products creates a different incentive.
Every item needs a title, description, features, materials, compatibility notes, dimensions, care instructions, and search-friendly language. That is exactly the kind of repetitive work automated text generation can accelerate. The danger begins when the system stops rephrasing supplied facts and starts filling gaps with plausible ones.
Shopify’s own documentation for its AI product-description feature, Shopify Magic, makes the distinction unusually clear. Merchants can provide a title and a few keywords, then generate a complete description. But Shopify also warns that generated copy can introduce product benefits or facts that the merchant never supplied, including details borrowed from similar products. Its guidance says merchants remain responsible for the accuracy of what they publish and should review generated text closely. See Shopify’s documentation on automatically generating product descriptions.
That warning gets more important as the catalog grows.
Plausible specifications are still invented specifications
A language model is very good at knowing what a product description usually sounds like. If the item is a jacket, the copy may naturally mention weather resistance. If it is a cable, the model may invent compatibility language. If it is a kitchen tool, it may add claims about dishwasher safety or materials because those details are common in similar listings.
The prose can sound more complete than the underlying record.
That creates a subtle reversal. Instead of the description being a readable version of verified product data, the description becomes a source of new claims that somebody now has to investigate after the fact.
The Federal Trade Commission’s general advertising guidance is boring but useful here: advertisers are responsible for express and implied claims, and material claims need a reasonable basis. Automation does not transfer that responsibility to the model.
The source record has to remain authoritative
The safest workflow is simple. Structured product data comes first: manufacturer specifications, measured dimensions, tested compatibility, ingredients, materials, warranty terms, and other facts that can be checked. Generated copy can then reorganize those facts into readable prose.
When the generated description adds something not present in the source record, it should be treated as an unverified suggestion, not as a discovered fact.
This matters because scale changes the consequences. One invented sentence on one listing is a correction. One invented attribute propagated across ten thousand SKUs becomes a catalog-level data problem.
Machine-written product descriptions are not inherently deceptive. They are a publishing tool. The problem begins when the smoothness of the language hides the difference between information supplied by the merchant and information guessed by the machine.
A huge catalog can be automated. Responsibility cannot.