Rapid Prototyping For Validating Innovative Concepts

AI ENABLES PERMISSIONLESS INNOVATION The review gauntlet that product orgs use to "ensure quality" often kills breakthrough ideas before they see the light of day. Strategy reviews, product committees, design approvals—each layer of gatekeepers favors safe, consensus-driven concepts over the risky, opinionated bets that create real innovation. AI prototyping is changing this dynamic entirely. Smart PMs are now bypassing traditional approval processes by building functional AI prototypes themselves. Instead of pitching abstract concepts to committees, they're: - Creating working prototypes in hours or days - Testing directly with real customers - Gathering concrete feedback and usage data - Iterating based on actual user behavior - Walking into review meetings with proof, not just PowerPoints The result? They're presenting stakeholders with tangible experiences and customer validation rather than hypothetical arguments. It's much harder to kill an idea when users are already loving the prototype. The new playbook: Build first, get permission later. When you have a bold product idea, don't let it die in committee. Use AI to prototype your vision, validate it with real users, then use that momentum to navigate the approval process from a position of strength. What innovative ideas are you sitting on that could benefit from this approach?

Show, Don’t Tell: Vibe Prototyping Is the New PM Superpower I've shipped hundreds of features—from tiny ones like tags to major launches like Rescale’s AI Physics—and one thing holds true: prototypes beat specs. Every time. Now, with AI, you can prototype at the speed of thought. I call it Vibe Prototyping—a way to build and validate product vibes before real investment. Using tools like ChatGPT and Replit, you can go from insight to working UI in hours. Here’s how I do it: (1) Extract needs (<1h): Use ChatGPT DeepResearch to synthesize user insights from Reddit, support tickets, research, etc. (2) Draft a spec (1h): Write your vision, constraints, and references, then turn it into a detailed PRD with ChatGPT. (3) Generate a working prototype (1h): Feed the spec into Replit and get a working prototype in minutes. (4) Validate the need (days): Share with users, design, and stakeholders. Iterate fast. Why this matters: - Speed > Slides: You validate in hours, not months. - AI is the new IDE: It turns your intent into working code instantly. - No prototype = no meeting: Talking in abstract is a waste. - This is the new PM stack: Ignore it and get left behind. Agile is starting to feel like waterfall. The future isn’t more process—it’s better intuition, faster loops, and showing instead of telling. Even companies like Shopify are shifting to this. PMs who build prototypes will ship 10x more, with 10x less friction. The rest will be stuck writing PRDs no one reads.

The last time I coded at production level was in 1996. This week, I built a social media advocacy reach calculator using Lovable that would have traditionally taken days of back-and-forth with engineering, requirement documents, and multiple cycles. It was completed in 30 minutes with just 5 iterations. The result? A pixel-perfect app that does exactly what I envisioned. It's not production feature. But a marketing tool that I intend to use to calculate reach on social media if you amplify your posts via employees, executives and channel partners. (Link to app below) Now, I could have asked an engineer to build it. But then it would have taken time away from building the core product. I have been in this situation so many times before. Build a custom demo. Or a landing page. Or an ROI calculator. Or experiment with that integration. Or create some cohort analytics. All non production stuff which I would loved to work on before handing off to engineering for production grade code. Instead, I always had to prioritize my ask, confirm it is essential, trade off something that is on the backlog and limit iterations. Even when the results were underwhelming, I’d justify continuing because we’d already invested so much—classic sunk cost fallacy. But that changes now.  📋 → ⚡ From PRDs to Rapid Experimentation The days of spending weeks crafting comprehensive specs are over. Instead of "think, document, build, test," we can now "think, build, test, refine" – compressing months of planning into hours of actual doing. When you can describe your vision in natural language and see it come to life in minutes, you validate assumptions with real usage, not theoretical scenarios. 🔧 Every PM is Now a Prototype Engineer No more playing translator between business and engineering. AI tools let us build functional prototypes ourselves – not just mockups, but working applications with real calculations and production-ready functionality. This means we can validate technical feasibility, UX, and business logic simultaneously, leading to better collaboration with engineering teams. 🎯 True MVPs: From Concept to Market in Days The reduced cost of being wrong about implementation details is liberating. We're moving from assumption-based planning to evidence-based iteration. The result? Engineering stays focused on shipping features users need. PM get to explore ideas without guilt. And when we do collaborate, it's on validated concepts worth their time. What other benefits do you see as a PM with these AI tools? Here is that app: https://lnkd.in/gK3FZfQx

I usually have to wait 2-6 weeks to get prototype carbon fiber parts… and that slows me down unfortunately, most CF vendors don't know how to hack something out they want to make it perfect they want to have a real aluminum tool they want to follow a strict quality process. but guess what.... when I'm early in a design, I don't need any of that. I'm looking for quick feedback by testing real parts with real materials in the real world I'm looking for rapid design validation. not with production-quality parts but with rough approximations of the final design. soo.... here's a quick 8 step process for rapid prototyping CF (carbon fiber) parts without any special tools. doing this, I can get parts in hand overnight. here's how I do it: 1. design and print double-sided mold, (male and female clamping mold). if you don't have a big enough printer, order from xometry.com or print in pieces and glue together. 2. rub mold with mold release. I found this wax-based type to work better than spray type. 3. cut CF sheet, use heavy duty shears, (the fabric will destroy regular scissors) 4. mix epoxy, and if you have access to a vacuum degasser, use that. if not, skip it. 5. pour epoxy into fabric, smooth with paintbrush, careful not to mangle the CF fabric, be gentle! if you didn't use a vacuum degasser, do a 'needle pour' by lifting cup high above mold to allow bubbles to escape in the thin epoxy steam 6. snip reliefs or darts into fabric to allow too relieve stress in corners. alternate fabric direction for strength. 7. squish CF mold between two molds. use heavy things to apply clamping force 8. take out of mold and trim with Dremel #rapidprototyping #design #engineering

Product managers used to overbuild in pursuit of perfection. Then we overcorrected, with raw MVPs. Today, AI prototyping gives us the tools to build better products—faster, and with more confidence. For years, validating ideas early was the goal—but it took too long. So we skipped discovery. We overbuilt based on gut. And we launched late—only to learn we were wrong. Then came MVPs. We shipped faster—but often learned less. Too lean to deliver value. Too early to earn trust. Today, there’s a better way: AI prototyping is unlocking the Build Smarter Loop. It’s a faster, more confident path to product learning: 1️⃣ Prototype to test assumptions -> Use AI prototyping tools (like v0, Bolt, Replit, Lovable) to quickly mock up key flows, feature ideas, and messaging. -> Validate your riskiest assumptions with internal teams, user testing platforms, or lightweight customer interviews—before you involve engineers. 💡 Catch bad bets early and explore multiple options without heavy lift. 2️⃣ Deliver a better product—faster and with more confidence -> Ship a lean version designed to validate learning goals, not just to “check the MVP box.” -> Because your discovery was fast and informed, your build is focused, intentional, and aligned. 💡 You launch faster without guessing—and with buy-in from users and stakeholders. 3️⃣ Learn and refine continuously -> Instrument usage to track how users interact with your product—ignore what they say, watch what they do. -> Close the loop by feeding these insights back into both your roadmap and your next round of prototyping. 💡 Every iteration gets sharper, driven by data—not gut feel. Final thought: AI prototyping enables you to improve what you launch—and how quickly you learn from it. — 👋 I’m Ron Yang, a product leader and advisor. Follow me for insights on product leadership & strategy.

Product leaders, stop hiding behind docs! If your team is still spending all their time in PRDs and product strategy docs, they're not operating in 2025. AI prototyping has literally changed the game. Here's how teams should do it: — THE OLD WAY (STILL HAUNTS MOST ORGS) 1. Ideation (~5% actually prototyped) “We should build X.” Cool idea. But no prototype. Just a Notion doc and crossed fingers. 2. Planning (~15% use real prototypes) Sketches in Figma. Maybe a flowchart. But nothing a user could actually click. 3. Discovery (~50% try protos) Sometimes skipped. Sometimes just a survey. Rarely ever tested with something interactive. 4. PM Handoff (~5%) PM: “Here’s the PRD.” Design: “Uhh… where’s the prototype?” PRDs get passed around like homework. 5. Design Design scrambles to build something semi-clickable, just so people stop asking “what’s the plan?” 6. Eng Start Engineering starts cold. No head start. They’re building from scratch because nothing usable exists. — WHAT HAPPENS - Loop after loop. Everyone frustrated. - Slow launches. Lots of guesswork. - And no one truly understands the user until it’s too late. — THE NEW WAY (THIS IS HOW WINNERS SHIP) 1. Ideation PMs don’t just write ideas. They prototype them. Want to solve a user problem? Click, drag, test. There. No waiting. No “someday.” You build it, even if it’s ugly. 2. Planning Prototypes are the roadmap. You walk into planning with a live flow, not a list of features. And everyone’s like: “Oh. THAT’S what you meant.” 3. Discovery Real users. Real prototypes. You send them a flow and you watch them break it. You’re not guessing anymore. You’re observing. 4. PM Handoff PMs don’t just hand off docs. They ship working demos alongside the PRD. No more “interpret this paragraph.” Just click and see it work. 5. Design Designers don’t start from scratch. They take what’s already tested, validated, and tweak it. Suddenly, “design time” is “refinement time.” 6. Eng Start Engineers don’t wait around. They start with something usable. If not, they prompt an AI tool to build it. And we’re off to the races. — If you want to see how AI prototyping actually works (and learn from expert Colin Matthews), check out the deep dive: https://lnkd.in/eJujDhBV