What is the belief, and where does it come from?
The belief is that AI-written product descriptions are always generic: the same reassuring adjectives, the same shape of sentence, copy that could sit on any competitor's page. It comes from experience. Stores that pasted a product name into a text generator got exactly that, and stores that bought bulk AI descriptions from a plugin got a catalog that read like one long paragraph with the nouns swapped.
The belief is a fair reading of that evidence. Any honest examination has to start by saying so, because a store owner who has seen generic AI copy is not wrong about what they saw.
What is true in the belief?
What is true is that a generator with no product data has nothing specific to say, so it says something plausible. Asked to describe a merino sweater with only the name to go on, it will produce warmth, comfort, versatility and craftsmanship, because those words fit any sweater. The output is generic because the input was empty. The same generator given the fibre weight, the gauge, the fit, the care instructions and the store's voice produces something a buyer can use.
It is also true that a system which is rewarded for producing text will fill a gap rather than admit one. That is the deeper cause: not the model, but the absence of a rule that says a claim needs a source.
What does the Product Catalog FTE's fact-gate actually do?
The Product Catalog FTE on ftes.ai writes under a fact-gate: every claim in a proposed title or description must map to a field in the catalog, a supplier sheet the store uploaded, or copy the owner approved before. The map is shown beside the draft in Approvals. A claim with no source is not written; the gap is flagged with the field name so the owner can fill it or leave it.
This changes the output in a way a better prompt cannot. Specificity now comes from the data, and the FTE's first proposal for a thin SKU is usually a field fill, not a rewrite: the material, the dimensions, the compatibility list. Once the fields exist, the description has something specific to say, in the store's voice, and it says only that.
Why does specificity come from fields rather than from writing?
Specificity comes from fields because a buyer's questions are about attributes: will it fit, what is it made of, does it work with what I own, how do I care for it. A description that answers those questions is specific by construction, however plain its sentences. A description that avoids them is generic however elegant. The Product Catalog FTE therefore treats the attribute set as the first draft of the description and the prose as the second.
This also explains why generic copy clusters in the same collections: they are the ones with the emptiest attribute sets. Fix the data and the copy problem shrinks before a word is rewritten.
How can a store check for itself?
A store can check the claim on any AI-written product page with two questions. First, for each sentence, where did the facts come from: a field, a sheet, approved copy, or nowhere? A sentence with no source is a guess dressed as a description. Second, did the writer flag anything it could not source? A writer that never flags a gap on a catalog with gaps is filling them.
On ftes.ai both answers are on the screen: the claim-to-field map beside each draft in Approvals, and the flagged gaps in the audit. A store that runs the check on a plugin's output and gets no answer has learned what it needed to know.
What remains true even with a fact-gate?
What remains true is that a fact-gate cannot invent a voice or supply craft. The Product Catalog FTE writes in the voice it was given, from the data it has. A store with no voice and empty attributes will get accurate, plain, sourced copy, which is better than generic copy and still not a flagship page. The flagship page is a writer's job, and the honest position is to say so and to use the Digital FTE to apply that writer's voice across the catalog.