I Should Be Charging $999 for This AI Prototyping Masterclass

Nadav Abrami (co-founder of Wix) breaks down the complete AI prototyping workflow for PMs. When to prototype, how to build divergent solutions, when to go high fidelity, how to hand off to engineers - all live demos using Dazzle. Full Writeup: https://www.news.aakashg.com/p/nadav-abrahami-podcast Transcript: https://www.aakashg.com/ai-prototyping-for-pms-complete-guide-nadav-abrami/ Dazl: https://dazl.dev/?utm_source=productgrowth&utm_medium=youtube --- Timestamps: 0:00 - Intro 1:33 - Guest introduction 1:46 - Will AI replace developers? 3:03 - When should PMs use AI prototyping? 6:07 - Problem space vs solution space 8:02 - Live demo: starting from your design system 10:12 - Ads 11:40 - Design system template workflow 19:53 - Building divergent solutions live 22:37 - How to prompt clearly 30:31 - Ads 33:16 - Visual editing vs prompting 52:05 - When to go high fidelity 58:21 - Engineer handoff 1:02:50 - PRD plus prototype: the new standard --- 🏆 Thanks to our sponsors: 1. Pendo: The #1 software experience management platform - http://www.pendo.io/aakash 2. Testkube: Leading test orchestration platform - http://testkube.io/ 3. Gamma: Turn customer feedback into product decisions with AI - https://gamma.app/?utm_campaign=prompt&utm_content=Aakash+Gupta&utm_source=LinkedIn 4. Product Faculty: Get $550 off the AI PM Certification with code AAKASH550C7 - https://maven.com/product-faculty/ai-product-management-certification?promoCode=AAKASH550C7 5. Mobbin: Discover real-world design inspiration - http://mobbin.com/aakash --- Key Takeaways: 1. AI prototyping doesn't replace problem space work - it accelerates solution space work. Before opening any prototyping tool, lock down the problem, the user story, and the rough shape of the solution. If you can't write all three in one paragraph, you're not ready. 2. Always start from your design system, not a blank page - Drop a screenshot of your existing product and ask the tool to recreate it. Save that as a team template. Every prototype you build from that point looks like it belongs in the product. 3. Build 3 to 4 divergent solutions before choosing one - The entire point of AI prototyping is that building a second and third version costs almost nothing now. We built two versions of the sentiment analysis feature live. Neither was perfect. Both were useful. That comparison is the point. 4. Use visual editing for fine-tuning, not prompting - Once you've picked the strongest direction, switch to direct visual editing. Move elements, match colours with the eyedropper, adjust spacing. It's faster because the result is immediate. 5. Single-page prototypes miss too much - Build the full end-to-end flow. The moment you start connecting pages, edge cases surface automatically. We found two edge cases in minutes that would have cost engineering time in sprint. 6. Prompt clarity beats prompt engineering - Any ambiguity in your prompt will get exploited statistically. Before running a complex prompt, paste it into a separate chat and ask it to find the contradictions. Fix those first. 7. Use discuss mode before building anything major - Don't ask the AI if it can do something. That always gets a yes. Ask what it thinks the right approach is. The answer is far more honest and useful. 8. High fidelity is for selling and usability testing - Low fidelity is for team exploration. Any prototype going in front of users needs to feel real, otherwise you get feedback about the roughness, not the experience. 9. The PRD and prototype should live together - The PRD covers edge cases, empty states, error conditions. The prototype covers the 90% flows. Together they leave zero open questions for engineers. If someone reads both and still has a question, something is missing. 10. The prototype is already standard code - A functional prototype built in Dazzle is a full server-side and client-side application. Download the project folder, drop it next to the production codebase, and tell Cursor to copy the interaction. Most of the implementation gets handled automatically. --- 👨‍💻 Where to find Nadav Abrami: LinkedIn: https://www.linkedin.com/in/nadav-abrahami-7507604/ 👨‍💻 Where to find Aakash: Twitter: https://www.x.com/aakashg0 LinkedIn: https://www.linkedin.com/in/aagupta/ Newsletter: https://www.news.aakashg.com #aiprototyping #aipm --- 🧠 About Product Growth: The world's largest podcast focused solely on product + growth, with over 200K+ listeners. 🔔 Subscribe and turn on notifications to get more videos like this.

Nadav AbramiguestAakash Guptahost

Feb 26, 20261h 16mWatch on YouTube ↗

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 ideas

AI 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 quotes

If 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

Will AI replace developers or PMs?When PMs should use AI prototypingProblem space vs solution space timingDesign-system/template-first prototypingDivergent solution exploration (multiple variants)Prompt clarity and “discuss/plan mode”Visual editing vs prompting vs code editingHigh-fidelity prototypes for selling and usability testsMulti-page flows and edge casesEngineer handoff: links, code export, Git, and AI-assisted copyingPRD + prototype as the new standardPM upskilling: understanding architecture and code concepts

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