Why do AI-generated UIs all look so generic? It's the workflow, not the prompt 🎨 A practical playbook for producing genuinely beautiful UIs. Title: Generating Beautiful UIs URL: 🎨 Overview This post lays out a practical methodology for generating beautiful UIs with AI. The thesis is that there's no single magic technique — what works is a disciplined workflow built on pre-defined design systems and fast iteration loops. ❓ Challenges Solved AI-generated UIs tend to come out generic and predictable. The post names the common failure modes. ・Dashboard-ification: turning everything into a dashboard ・Nested cards: redundant cards inside cards ・Instruction leakage: prompt instructions bleeding into the on-screen copy ・Weak compositional logic: layouts that break down and lack beauty or resonance 💡 Methodology & Proposed Approach The post recommends a methodical workflow built from these steps. ・Use component libraries: shadcn/ui via MCP integration ・Pre-define the design system: keep design tokens as readable files to prevent hallucination ・Enforce constraints: use Tailwind config to block drift ・Iterate with vision models: feed screenshots to run a visual improvement loop ・Generate multiple options before committing ・Test with hostile, realistic data during development 🌍 Use Cases / Experimental Results Combining fast inference with a disciplined workflow turns AI from a gimmick into a real prototyping accelerator. ・Codex-Spark runs at ~1,200 tokens/sec on Cerebras, generating several design options in minutes ・With proper tooling, components compile on the first attempt ・Tighter feedback loops reduce wasted tokens The conclusion: AI is a fast, overconfident junior designer that still needs human art direction, not an autonomous replacement. #UIDesign# #GenerativeAI#