I recently tested two versions of the same product description to see whether a small wording change would affect how an AI evaluated it.
The first version was simple:
Gives developers access to multiple AI models through one API.
The second version added one sentence:
TokenBay gives developers access to multiple AI models through one API. It is most useful for teams that regularly compare or switch models, but may be unnecessary for projects that only use one provider.
I expected the second version to make the product sound less appealing. After all, product copy usually tries to remove doubt, not introduce it. Saying that some people may not need the product felt a little like putting a warning label on the homepage.
The model reacted in the opposite way.
Its recommendation became more specific and, strangely, more confident. Instead of describing the product as a generally useful multi-model platform, it explained that TokenBay made sense for teams testing several providers, comparing outputs, or trying to avoid maintaining separate accounts and integrations. It also noted that a small project committed to one model would probably not benefit as much.
Nothing about the product had changed. The model simply had a clearer boundary around when the product was useful.
That made me realize that limitations can give an AI something important: a reason to rule a product out.
Most product descriptions only explain why someone should use the product. They list features, benefits, and broad claims about who it is for. The result often sounds positive but vague. A platform is “flexible,” “powerful,” and “built for modern teams,” which could describe half the software products currently asking for my email address.
A limitation narrows the picture. Once the description says that the product may be unnecessary for single-provider projects, the intended user becomes easier to identify. The model can compare the product against a real situation instead of trying to interpret a list of features.
I do not think the lesson is that every product page should suddenly lead with everything the product cannot do. That would be honest, but perhaps not excellent marketing. The useful part is giving enough context for the product to be evaluated properly.
For example, these two statements communicate very different levels of information:
Supports multiple AI models.
and:Designed for teams that regularly test or switch models and do not want to maintain separate provider integrations.
The first tells you what exists. The second tells you when it matters.
Adding a limitation makes that distinction even clearer:
Probably unnecessary if your application only relies on one provider.
Now the product has a recognizable shape. It is no longer trying to be useful to everyone, which makes the recommendation feel less like generic promotion and more like an actual judgment.
This may matter more as people increasingly ask AI systems to compare tools for them. A model needs enough information to decide not only why a product fits, but also when another option would make more sense. Without that boundary, the safest answer is often vague: the product “could be useful depending on your needs.”
With a clear limitation, the model can say something more helpful.
The part I found most interesting was that the limitation did not weaken the recommendation. It made the recommendation easier to justify.
Maybe good product descriptions should not only help a model rule the product in. They should also help it rule the product out.
That sounds slightly uncomfortable from a marketing perspective, but it may be what makes the recommendation believable.
Top comments (2)
This reminds me of the Chinese stratagem "to capture something, first let it go." Telling an AI why it shouldn't recommend something somehow makes the recommendation stronger. That's a really clean insight — and honestly it works on humans too. Great piece.
I hadn’t made that connection, but “to capture something, first let it go” describes it surprisingly well. Funny how the same persuasion tactic seems to work on both AI and humans