AI Product Descriptions Get the Specs Wrong: Auditing Generated Copy Before It Costs Returns
Generated descriptions invent dimensions, materials, and compatibility. The errors read fluently, which is why they survive review and turn into returns and support tickets.
A generated product description rarely fails by sounding robotic. It fails by stating, in perfect sentences, a dimension the product does not have, a material it is not made from, and a compatibility it does not offer. Fluency is the camouflage. A human copywriters typo gets caught in review. A confident invented specification gets published.
Operators find out from returns. The customer measured, the shelf did not fit, the description said it would. That return costs freight both ways and the product often cannot be resold as new, all for a sentence nobody wrote and nobody checked.
Where the inventions cluster
Numbers go first: dimensions, weights, capacities, counts in a pack. Generation fills a plausible figure when the source data is missing, rather than leaving a gap. Materials are second, especially blends, where cotton becomes cotton rich in one product and a precise percentage appears in another, sourced from nowhere. Compatibility claims are third: works with, fits, suitable for, attached to models and systems the feed never mentioned.
The pattern to understand is that the model completes the genre. Product descriptions in its training data have dimensions, so this one gets dimensions. If your feed has holes, generation will pave over them silently unless the workflow is built to stop it.
Audit the data, then the copy
Start upstream. Pull the source attributes for a sample of products and diff them against the published descriptions, field by field. Any number in the copy that is not in the source is a defect, regardless of whether it happens to be right. That rule is strict and it is the only one that scales, because checking truth product by product does not.
Then set the generation rules in plain terms: use only supplied attributes, write nothing where data is absent, flag missing attributes back to the merchandising team instead of improvising. A description that is two honest sentences beats one that is five sentences and a return. Track the flag rate. A rising pile of missing dimensions is a data problem the AI has usefully exposed.
Measure it in returns language
Tag returns and support contacts that cite description mismatches for a month before and after the audit. That is the number the business understands, not a copy quality score. In our experience reading these audits, mismatch returns are a small share of volume and a large share of cost, because they concentrate in bulky, expensive to ship products, exactly where a wrong dimension does the most damage.
Keep a human review step for high value and high return cost products even after the process improves. Automation can draft. Accountability for a published specification still needs a person who checked it against the thing in the warehouse.
Related reading
If this topic is useful, these earlier guides go deeper on connected questions: AI Recommendations on Out of Stock Pages: Helpful Detour or Dead End, AI Chat Answers That Invent Shipping Policies, and How to Audit Them, and When AI Search Returns Nothing: Synonyms, Slang, and Zero Results.
Frequently asked questions
Why do AI descriptions invent specifications?
When source data is missing, generation tends to complete the expected pattern of a description rather than leave gaps. Missing dimensions, materials, and compatibility get filled with plausible text unless the workflow forbids it.
How should generated descriptions be audited?
Compare a sample against source attributes field by field. Any number or material claim in the copy that is absent from the source counts as a defect, even if it happens to be correct.
What rule prevents most invented specs?
Instruct the system to use only supplied attributes, publish nothing where data is missing, and flag gaps back to merchandising. Shorter honest copy outperforms fluent invented copy once returns are counted.
Which products need human review most?
Bulky, expensive, and high return cost products, and anything where compatibility or dimensions decide the purchase. Those are where a wrong sentence costs the most.