ChatGPT for product descriptions: a workflow that scales

ChatGPT can write a product description in four seconds. It can also write four hundred descriptions that all sound like the same mildly enthusiastic intern, which is worse than useless when Google is deciding whether your category page deserves to rank. The gap between those two outcomes is the workflow, not the model. Here is how we use ChatGPT for product descriptions on real stores without ending up with a catalog of grey goo.

This is written for someone with a few hundred to a few thousand SKUs, where writing each one by hand is not realistic and pasting them straight from ChatGPT is a quiet SEO mistake.

The problem with the four-second description

Ask a model for a description with nothing but the product name and you get confident fiction. It will invent a material, guess a use case, and reach for “elevate your everyday” because that phrase appears in half its training data. Two things break at once: the copy is factually loose, and it is generic enough that a hundred other stores are publishing near-identical text. Thin, duplicative product copy is exactly what Google’s helpful-content updates target.

So the fix is not a better prompt in isolation. It is feeding the model real facts and forcing it out of its default voice.

Feed it specifics, not just a name

The single biggest quality jump comes from the input. Instead of “write a description for the Alpine 20L Backpack,” give the model the attributes you already have in your product data: material, capacity, weight, three real features, and who it is for. A prompt built from your own spec sheet produces copy that is accurate because it has nothing to hallucinate about.

A workable template looks like this:

• Product name and category.
• Five to eight hard facts (dimensions, material, weight, warranty).
• Two differentiators — what this one has that the cheaper one does not.
• The buyer and the moment (“weekend hikers who commute by bike midweek”).
• A length cap and a banned-words list.

That last item matters more than people expect. We keep a standing list of words the model is not allowed to use (“elevate,” “seamless,” “game-changer,” “in today’s world”) and paste it into every prompt. It removes ninety percent of the tells in one move.

Batch, but keep a human on the seam

For volume, you run this through the API against a spreadsheet of attributes, not by pasting one product at a time. One row per SKU, columns for each attribute, and a script that fills the template and calls the model. A few thousand descriptions take an afternoon of compute instead of a month of typing.

The part people skip is the review pass, and it is the part that saves you. We spot-check roughly one in ten generated descriptions and read every one in the top-selling category. You are looking for two things: a fact the model got wrong, and a sentence that shows up verbatim across products. Fix the prompt when you find a pattern, not the individual line. The goal is a template that produces good copy at scale, not hand-editing four thousand paragraphs.

Where this fits on an OpenCart or WooCommerce store

On the platforms we support, the generated text has to land back in the catalog cleanly, and that is its own small project. Mapping a spreadsheet of descriptions to the right product IDs, handling multi-language stores, and not overwriting descriptions a human already tuned — that plumbing is where these projects actually stall. We build that import step as part of our AI work for OpenCart, so the copy generates and lands without someone pasting for a week.

If you are on WordPress, the same logic applies and we have written up what holds up and what does not in AI content for WordPress — what actually works. The stack changes; the discipline does not.

What to actually take away

Treat the model as a fast writer with no knowledge of your products, because that is what it is. Give it your real attributes, ban its favorite words, run it in batch against a spreadsheet, and keep a human reading the seams. Do that and ChatGPT earns its place in the catalog workflow. Skip the review pass and you have automated the production of content Google is actively trying to bury. We would rather help you set up the first version than clean up the second — that is what our OpenCart support and AI integration work is for.