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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 27, 20261h 16mWatch on YouTube ↗

CHAPTERS

  1. AI won’t replace developers—unless you already understand how to build

    Nadav frames AI as a powerful tool rather than a replacement for engineering. The core idea: AI amplifies existing capability, so people who already understand how software is built will get the most value from AI-assisted building.

  2. When PMs should use AI prototyping: ideation, validation, and speed-to-learning

    They lay out why AI prototyping tools are becoming a default part of PM workflow. The value is functional, testable experiences that dramatically reduce the cost of experimentation compared to traditional prototype-building.

  3. Avoiding the trap: problem space vs solution space (research still matters)

    Aakash raises the concern that rapid prototyping can push teams prematurely into solutions. Nadav agrees: strong research, user understanding, and defining the feature shape should come before generating prototypes.

  4. Live demo kickoff: recreate an existing product screen as your starting template

    Nadav demonstrates a key workflow: start from a screenshot of the existing product (LinkedIn) to generate a high-fidelity baseline. This creates a reusable foundation that keeps prototypes visually aligned with the real product.

  5. Under the hood: design system CSS, code generation, and validation steps

    They pause to interpret what Dazzle is doing technically (theme CSS, components, validation). The emphasis is that PMs benefit from understanding the basics of what the tool produces and how it verifies correctness.

  6. Visual fidelity improvements: eyeballing colors, images, and layout via visual editing

    Nadav fixes mismatched colors by using an image reference and an eyedropper-like workflow. This segment highlights the speed of direct manipulation (visual editing) to dial in UI accuracy without re-prompting the AI repeatedly.

  7. Add the first feature: sentiment analysis on posts (fast functional enhancement)

    They prompt Dazzle to add a lightweight sentiment analysis feature to LinkedIn posts. The broader point is that PMs can now prototype “small but meaningful” features without waiting on engineering bandwidth.

  8. Divergent solutions: rapidly exploring multiple UX implementations

    Aakash emphasizes that the real leverage is generating multiple distinct solutions, not perfecting the first. They create a second variation: a separate sentiment summary module with an overview and drill-down concept.

  9. Prompting mastery: clarity beats prompt-engineering theatrics

    Nadav argues PMs don’t need elaborate system prompts; they need unambiguous intent. He explains misinterpretation risk, recommends “discuss mode” for major changes, and suggests using an LLM to detect contradictions before running prompts.

  10. Editing workflow: visual edits vs prompting vs code (and what Cursor gets wrong)

    They compare three editing modes: direct visual manipulation, natural-language prompting, and code edits. Nadav stresses immediacy: for many changes, visual edits are faster and more deterministic than prompting, and he contrasts this with Cursor’s “visual prompting” loop.

  11. Building a multi-page, high-fidelity prototype to reduce usability and alignment risk

    They extend the prototype into a dynamic post detail page with sentiment explanations and comment breakdowns. Then they discuss when high-fidelity prototypes are worth it: selling internally, fundraising, and conducting realistic usability tests.

  12. Engineer handoff: publish link, share code, and keep specs inside the project

    Nadav explains that a working prototype answers most engineering questions by demonstrating behavior. Beyond sharing links, teams can export standard code, use adjacent-code copying with developer AI tools, and embed spec context (PRD snippets) directly into project files.

  13. PRD + prototype becomes the new standard: 90% flows in prototype, edge cases in PRD

    They argue PRDs aren’t dead, but their role changes: the prototype carries the main experience, while the PRD captures edge cases and completeness. Done well, PRD plus prototype should leave engineering with zero unanswered questions.

  14. The future: PMs must level up technical understanding as lines blur

    Nadav forecasts that AI will blur the line between developers and tech-savvy builders, more than fully replacing engineers. He advises PMs to build technical fluency by interrogating code with AI and gradually contributing small changes, improving collaboration and speed.

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