Read the original at Lenny’s Newsletter ↗lennysnewsletter.com · subscriber post
By Lenny Rachitsky · lennysnewsletter.com · @lennysan on X · YouTube · LinkedIn
Colin Matthews explains how product teams can move beyond individual AI prototyping by building shared component libraries, using baselines and forks, and matching prototype fidelity to each stage of the product development lifecycle. The post argues that consistent, brand-matched prototypes and clear team workflows are what turn AI prototyping from scattered experiments into a repeatable practice.
Subscriber post — summary only01Key takeaways
- Build component libraries from screenshots, browser extensions, or code to keep prototypes on-brand and consistent.
- Use a baseline prototype of your current product, then fork it to explore ideas without rebuilding from scratch.
- Match prototype fidelity to context: mid-fi for engineers, high-fi for executives and customer-facing pitches.
- Use prototypes as communication tools across discovery, alignment, PRD review, user interviews, and engineering scoping.
- Embed real logos via SVG or image links rather than letting AI tools redraw them, which looks unprofessional.
02Frameworks mentioned
Summary and takeaways written by PM Atlas; quotes are short excerpts. © the original author.