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Aakash GuptaI Should Be Charging $999 for This AI Prototyping Masterclass
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At a glance
WHAT IT’S REALLY ABOUT
AI prototyping for PMs: design-system templates, divergence, handoff workflows
- AI won’t magically let non-technical people ship production apps, but it can radically accelerate prototyping and small internal tools—especially for PMs who can clearly specify behavior.
- The recommended PM workflow is: research the problem space first, then generate multiple functional prototype variations, pick one, visually refine it to high fidelity, and validate with real users.
- Starting prototypes from your existing design system (or a screenshot/template) increases fidelity, speed, and reusability across teams, and may benefit from designer involvement.
- Dazzle’s differentiators are a full server-side + client-side app output, deep inspection/visual editing with immediate code persistence, and exposing app state/debugging context to the AI agent.
- PRDs aren’t replaced by prototypes: prototypes should cover the main flows, while PRDs document edge cases and constraints; together they should eliminate engineering questions at handoff.
IDEAS WORTH REMEMBERING
5 ideasAI prototyping is most valuable when it produces functional experiences, not just screens.
Nadav emphasizes that playable prototypes let users and stakeholders feel real interactions, making validation faster than waiting for production builds and often faster than purely design-only prototypes.
Do not skip problem discovery—prototype after research, not instead of it.
The conversation pushes back on “jumping into solution space” by stressing research, user conversations, and clear user stories before generating prototypes.
Start from your product’s design system (or a screenshot-based template) to accelerate fidelity and reuse.
Recreating an existing UI first avoids blank-page prototyping, makes new features easier to place in-context, and creates a reusable base template for the organization.
Generate 3–4 divergent solutions quickly, then perfect the winner.
The “magic” is speed of exploration: use AI to produce multiple implementations, evaluate by playing with them, then refine one with visual edits and targeted prompting.
Prompt engineering matters less than prompt clarity—treat AI like a literal, non-pushy teammate.
AI won’t warn you that requirements are contradictory; ambiguous phrases get misinterpreted, so use “discuss/plan mode” and even ask an LLM to identify contradictions before building.
WORDS WORTH SAVING
5 quotesIf you can't build a production app without AI, it's gonna be really hard to use AI correctly to build a production app.
— Nadav Abrami
What they got now is a virtual developer.
— Nadav Abrami
It’s not about going technical. It’s about going clear.
— Nadav Abrami
Anything that can be misinterpreted will statistically be misinterpreted.
— Nadav Abrami
Cover the main 90% flows with the prototype, and make sure that all of the edge cases are in the PRD.
— Nadav Abrami
SPEAKERS
Nadav Abrami
guestCo-founder of Wix and product/technical builder focused on AI prototyping tools (Dazzle).
Aakash Gupta
hostProduct-focused podcast host and creator of aakashg.com newsletter bundle, interviewing guests about AI and product building.
High quality AI-generated summary created from speaker-labeled transcript.
