Figr Design Blog

Design literacy compounds. Each prompt teaches something. Each variation expands vocabulary. Each comparison sharpens judgment.

Validation was traditionally gated behind resources PMs did not control. When validation costs days instead of weeks, the gate dissolves. PMs validate before they pitch. They fail fast without failing publicly.

The PRD does not need to wait in the design queue. The prototype does not need to look generic. The stakeholder review does not need to be abstract. Your next feature can have a stakeholder-ready prototype by end of day. Not a wireframe. Not a rough sketch. A prototype in your product's actual design language with edge cases considered. (Want a simple test? Pick one PRD and try it end to end.)

A feature shipped Tuesday, but by Thursday a bug exposed a missed “offline during upload” scenario—triggering a costly mini-crisis: two days of engineering rework, a day of design, and QA, eating 25% of total dev time. The piece argues PMs miss edge cases because complex features have too many states to hold in working memory, and fixing requirements defects after launch is far more expensive than catching them during design. Tools like Figr use pattern intelligence to surface the full set of states (empty, loading, errors, degraded networks, interrupts, dependencies) before you ship.

Generic AI prototypes often look like templates, not your product so meetings derail into styling debates and PMs waste huge time “fixing” outputs, losing speed, context, and credibility. The fix is tools that first understand your actual design system (fonts, spacing, colors, components) so prototypes match your product from the start and you stop paying the “Generic Output Tax.”

AI doesn’t replace PM designer collaboration it upgrades it: PMs use AI to show visual intent fast, and designers use their expertise to refine, polish, and ensure quality/accessibility. The result is less guessing and weeks of alignment collapsing into same-day iteration.