Making AI Prototypes Work Inside a Real Design System
A hands-on look at why AI builders produce lookalike screens and the manifest and MCP setup that keeps prototypes tied to real components.
Pierre Silva1 min read
Why Do AI Prototypes Keep Missing the Design System?
Jiao's hands-on guide argues that AI prototypes look finished but fail once developers open the code, because builders recreate components rather than using the real ones. The author tests a popular tool, shows where its output drifts from the source, and proposes a fix: a generated component manifest that lists every property, value and default, reachable by the agent through MCP. The article keeps the focus on practical checks rather than hype.
For brand builders, the point is that a convincing prototype is not the same as shippable work, and planning based on it creates expensive rebuilds. Design leads, product managers and design engineers who are being asked to speed up with AI will find a clear process for testing tools before committing. It is worth a read before your team sets dates on an AI-generated screen.
Source: uxdesign.cc · Originally published
