Engineering Blueprint
AI-Assisted UX Discovery with a Coding Agent — The Rejection Loop Method
Cut design rework cycles by grounding UX concepts in actual product data and metrics before engineering touches them—using an AI coding agent to enforce testability from discovery.
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What does this do
Most "AI in design" advice optimizes the wrong thing. It celebrates generation — faster mockups, more variants — when the real bottleneck in UX discovery is rarely generation. It is *grounding* (does this idea survive contact with the product's data?) and *rigor* (is this idea even testable?). Without those, a discovery sprint produces opinionated comps that get rebuilt three times by engineering and ship with placeholder data, or worse, never ship at all because no one named a metric.
How It Works
A 7-step loop, run with an AI coding agent as the workshop partner: (1) frame from a quantitative scorecard plus business and SEO non-negotiables, (2) force every opportunity into a structured hypothesis-card with a primary metric, (3) ground each design region in the actual product schema, (4) generate mid- to high-fidelity *annotated* concepts conditioned on a real design system, (5) run a cross-model critique pass that returns "top 5 to change, ranked by impact," (6) capture as a shareable summary plus image, (7) exit with testable next steps and named owners.
About This Blueprint
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- Computer Software