AI Written Product Descriptions, Accuracy, and the Returns They Cause
Generated descriptions speed up catalogue work and quietly invent details. How to keep AI copy accurate, and why inaccuracy shows up in the returns data.
AI drafting has made it cheap to give every product a full description, including the long tail lines that used to get two lines and a copied bullet list. The risk is not tone. It is invention: materials, care instructions, dimensions, and compatibility claims that sound plausible and are not in the source data.
Inaccurate descriptions do not fail loudly. They fail as returns, support questions, and reviews that say not as described. This guide covers a control process that keeps the speed and removes most of the invention.
Generate from source data, never from the product name alone
A generator given only a product name and a category will fill gaps from statistical habit: the most common material, the usual care advice, the standard feature set. Given the full specification record, it mostly rearranges facts. The input discipline is the accuracy control.
Define the required source fields per category before generation starts: materials, dimensions, care, compatibility, and contents. Products missing a required field should queue for data completion, not for more creative generation.
Build a human review step that looks for invention
Reviewers should check generated copy against the specification, claim by claim, not read it for fluency. Fluent invented copy passes a casual read easily. A checklist of verifiable claims per paragraph is what catches the added pocket, the wrong lining, the invented certification.
Sample review is acceptable for low risk categories once error rates are known and low. New categories, new suppliers, and any product with safety or sizing claims should stay on full review until their error pattern is understood.
Watch returns and questions as the accuracy signal
Tag returns and pre purchase questions with reason codes that can expose description problems: wrong size expectation, material different, feature missing, not compatible. A rising not as described rate in a category that recently gained generated copy is a finding, not a coincidence.
Feed those cases back into the generation process as named examples. A short list of real inventions, with the source data that should have prevented them, improves prompts and field requirements more than any general instruction to be accurate.
Keep descriptions maintainable after publication
Specifications change: a supplier swaps a material, a dimension is corrected, a bundle loses an item. Generated descriptions must be regenerated or edited when their source data changes, through the same change process as prices and stock, or the catalogue slowly fills with confident, outdated copy.
Record which descriptions are generated, from which data version, and when they were last checked. Without that record, a specification change cannot find the descriptions it affects, and accuracy work becomes a full catalogue reread every time.
Related reading
If this topic is useful, these earlier guides go deeper on connected questions: When AI Recommendations Get Prices and Stock Wrong, AI Agents Reading Your Shipping Policy: Write One They Cannot Misread, and When AI Search Summaries Rewrite Your Product Claims.
Frequently asked questions
What is the main accuracy risk in AI descriptions?
Invention of plausible details that are not in the source data, such as materials, features, and care instructions. It happens when generation starts from a name and category instead of a complete specification.
How should generated copy be reviewed?
Claim by claim against the specification, with a checklist, not as a fluency read. Full review for high risk categories, and sampled review only where measured error rates are already low.
How do description errors show up in the business?
As not as described returns, pre purchase questions the description should have answered, and reviews correcting the listing. Tracking those reason codes turns accuracy into a visible, manageable metric.
Should AI descriptions be labelled as AI written?
Policies vary, but accuracy responsibility does not. However a description was drafted, the store publishes it and owns its claims. Process controls matter more than the label debate for shoppers.