Language inconsistency breaks AI products. That is the central argument of this piece from UX Collective, and it is built on a foundation worth understanding: the 2017 Google paper 'Attention Is All You Need,' which introduced the transformer architecture now underlying every major LLM. The author explains that models represent words as vectors, points in geometric space, where meaning lives in proximity. 'Invoice,' 'payment,' and 'receipt' cluster together. So do 'dashboard,' 'metric,' and 'report.' When your team calls the same concept three different names, the model does not flag the conflict. It picks a definition, confidently, and ships it.
The practical consequence surfaces in a client meeting the author describes mid-article. A live demo stalls when a client stops to ask what 'intent' actually means, because the LLM output labeled with that word meant something different to everyone in the room. That is the moment the piece earns its argument: when AI is in the loop, a vocabulary disagreement stops being a coordination problem and becomes a product defect. The author's solution is a data model, not a database schema, but a shared document that names every entity in your system, defines it in one sentence, and lists its attributes. The example used is a food delivery app with eight entities: User, Restaurant, Menu Item, Order, Driver, Delivery, Payment, and Review.
The walkthrough of building that model from scratch is the reason to read the full piece. It is not theoretical. The author moves from entity naming to attribute definition to relationship mapping, and the decisions made at each step directly constrain what an AI can infer correctly. If your team is using LLMs in any product context and has not aligned on a shared vocabulary before prompting, this article is the diagnostic you are missing.
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