Why AI Tools Like Claude Generate Only 'Best Case' UI Screens

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AI models such as Claude consistently produce idealised interface screens because publicly available UI images are almost exclusively polished marketing assets, not real-world states. This training data bias means models default to fully loaded, error-free screens rather than generating empty, loading, offline, or error states. The practical consequence for product teams is a systematic underestimation of scope: a project quoted at 28 screens can balloon several times over once all required states per screen are accounted for. The author argues that UI states are not optional add-ons but integral design decisions that must be defined early, since retrofitting them onto finished screens often produces incoherent layouts. Similarly, UI component libraries built from AI-generated screens inherit the same gap, omitting critical interactive states like disabled, loading, or indeterminate that will inevitably surface during development.

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