How to humanize AI product descriptions
Add what the spec sheet cannot say, and verify every claim. Generated descriptions restate the manufacturer's blurb in different words, which every other retailer is also doing, and they invent specifications with total confidence. Say who it suits and where it falls short.
Typical length · 80–200 words each, across catalogues where hundreds are needed at once. · Last reviewed 16 August 2026
Before and after
Before
This premium wireless headset delivers exceptional sound quality and unparalleled comfort for the discerning listener. Featuring cutting-edge technology and a sleek modern design, it is the perfect choice for music lovers everywhere.
After
Over-ear, 34 hours on a charge, and heavy enough at 380g that most people notice after a couple of hours. The noise cancellation is strong on engine drone and ordinary on voices, which makes it a commuting headset rather than an open-plan-office one.
Five unverifiable adjectives became a weight, a battery figure and a limitation. The second version tells you who should not buy it, which is the information a buyer is actually looking for.
The duplicate-copy problem is older than AI
Most retailers publish the manufacturer's supplied description, which means the same paragraph appears on dozens of sites. Generated copy was adopted largely to solve that, and it substitutes a subtler version of the same problem: every retailer now runs the same source blurb through a similar model and gets a differently worded restatement of identical content.
The text is no longer byte-identical, and it still carries nothing the manufacturer did not say. Nothing is added for the buyer, and nothing distinguishes one listing from another except word choice.
The way out is information the spec sheet does not contain. Who the product suits, what it is bad at, what it compares to, what people return it for. All of that comes from your own returns data, reviews and staff, and none of it can be generated.
Say what it is not for
The single most useful sentence in a product description is the one naming who should buy something else. It is also the sentence no generated draft will write, because the source material is marketing copy and marketing copy has no limitations in it.
Buyers read it as credibility, and it reduces returns, which is a commercial argument rather than an ethical one. A headset described as unbeatable in every respect tells a buyer nothing; one described as excellent on engine drone and ordinary on voices tells them whether it fits their commute.
Invented specifications are the real hazard
This is the part that separates product copy from every other format on this site. A language model asked to describe a product will confidently produce dimensions, materials, battery figures and compatibility claims, and it has no way to know any of them. They are plausible, which is exactly the property that makes them dangerous.
Published as fact on a retail page, an invented specification is not a writing problem. It is a returns cost, a support burden and potentially a consumer protection matter, and the customer discovers it after paying.
So the rule is narrow and absolute: specifications come from the supplier data, never from generated prose. Use a rewrite on the descriptive sentences around them and leave the numbers where they came from.
At catalogue scale, the checking is the job
The appeal of generated descriptions is volume — hundreds of listings that would otherwise take weeks. That is real, and it moves the work rather than removing it: the constraint becomes verification rather than writing.
Practically, that means keeping the generated portion to sentences that carry no checkable claims, pulling every figure from the product database, and sampling the output rather than trusting it. A catalogue where nobody read the copy is a catalogue where the errors are still there and simply undiscovered.
Formats with the same problem
Press releases — the other place a generated figure becomes a claim you cannot take back.
Technical documentation — where specifications must come from the source data, for the same reason.
Questions
- Will Google penalise AI-written product descriptions?
- Search guidance has consistently framed the question as whether content is helpful rather than how it was produced. Restating the manufacturer's blurb is unhelpful whoever writes it, and that is the actual risk here rather than the involvement of a tool.
- How do I make hundreds of descriptions distinctive?
- From data you already have and competitors do not — returns reasons, review themes, what your staff tell customers, which products are bought together. That is the material that cannot be generated, and a short sentence of it per listing does more than a fully rewritten paragraph.
- Is it safe to let a tool write specifications?
- No. This is the one hard rule on the page. Models produce plausible dimensions and compatibility claims with complete confidence and no source, and published as fact they become a returns and compliance problem rather than a stylistic one.
- Do detectors matter for product copy?
- Very little. Descriptions are short, detection on short text is unreliable, and no retailer is scanning its own catalogue. The commercial risks here are duplicate content and wrong facts, neither of which a detector measures.
What to watch for
- Never let a tool generate a specification. Invented dimensions, materials and compatibility claims are the failure mode here, and they reach customers as fact.
- Consumer protection rules apply to product claims. A generated superlative can be a regulatory problem, not just a stylistic one.
- Check that a rewrite has not dropped a legally required disclosure — allergens, safety warnings, country of origin.
If your writing gets flagged
Rewriting for rhythm and specificity tends to lower detection scores, because that is what detectors read as human. It is not a guarantee — detectors disagree with each other and change without notice, and we do not promise a result from any of them.