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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.

The conversation pushes back on “jumping into solution space” by stressing research, user conversations, and clear user stories before generating prototypes.

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.

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.

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.

Nadav argues that once a prototype exists, many tweaks are faster via direct visual edits with instant persistence than waiting for a model to implement changes from a prompt.

High-fidelity prototypes help gain buy-in (leaders “see it”) and produce more valid usability feedback than low-fidelity or “Frankenstein” mixed-fidelity flows.

Because the output is standard code and publishable, engineers can review behavior via a link and optionally use AI coding tools (e.g., Cursor) to replicate the feature into the real codebase.

Prototypes communicate behavior faster than text, while PRDs remain critical for non-happy paths (empty states, too-many-items cases, constraints) and to remove remaining ambiguity.

Nadav predicts AI will blur roles: PMs may contribute small changes via AI, but the key is understanding components, data flow, and architecture to direct tools effectively and collaborate better.