Rapid Prototyping Techniques

What if the best solutions for your process started with cardboard? When testing new ideas or improvements, jumping straight to high-cost, permanent solutions can be risky—and expensive. That’s where cardboard engineering comes in. Cardboard is one of the simplest, most cost-effective tools for rapid prototyping and testing ideas. It’s lightweight, easy to shape, and lets you visualize, test, and refine your concepts before committing to more expensive materials. Why Cardboard Is Perfect for Prototyping: 1️⃣ Low-Cost Experimentation Testing with cardboard lets you try multiple iterations of a design without worrying about material costs. 2️⃣ Fast Feedback Loops You can build and modify a prototype in minutes, gathering instant feedback from your team or operators. 3️⃣ Hands-On Collaboration Cardboard prototypes allow teams to actively engage with ideas, making it easier to identify issues or opportunities for improvement. 4️⃣ Visual Validation Sometimes, seeing a physical model highlights challenges that wouldn’t be obvious in a drawing or plan. How to Use Cardboard for Lean Improvements: 🔍 Test Workstation Layouts Use cardboard cutouts to mock up layouts and placement of tools, parts, and equipment. Adjust until everything flows smoothly. 📦 Simulate Material Flow Prototype racks, bins, or carts to ensure materials are stored and moved efficiently before building them with more durable materials. 🛠️ Design Fixtures or Jigs Create cardboard versions of fixtures or jigs to test their functionality in the process. Refine the design before investing in the final version. 📐 Test Ergonomics Mock up equipment or workstation designs with cardboard to test ease of use, reach, and operator comfort. Example of Cardboard in Action: A manufacturing team wanted to redesign a workstation to reduce operator motion. Instead of committing to expensive reconfigurations, they used cardboard to prototype the layout. After several iterations, they found the optimal setup, reducing motion by 25% and saving hours of work. Cardboard isn’t just for packaging—it’s a powerful tool for testing and refining your ideas. By prototyping with low-cost materials, you can experiment, learn, and improve quickly without breaking the bank.

This is how Anthropic decides what to build next—and it's brilliant. Instead of endless spec documents and roadmap debates, the Claude Code team has cracked the code on feature prioritization: prototype first, decide later. Here's their process (shared by Catherine Wu, Product Lead at Anthropic): Step 1: Idea → Prototype Got a feature idea? Skip the spec. Build a working prototype using Claude Code instead. Step 2: Internal Launch Ship that prototype to all Anthropic engineers immediately. No polish required—just functionality. Step 3: Watch & Listen Track usage religiously. Collect feedback actively. Let real behavior, not opinions, guide decisions. Step 4: Data-Driven Prioritization - High usage + positive feedback → roadmap priority - Low engagement or complaints → back to iteration This "prototype-first product shaping" flips traditional product development on its head. Instead of guessing what users want, they're measuring what users actually use. The beauty? They're dogfooding their own tool to build their own tool. The feedback loop is immediate, honest, and impossible to ignore. The takeaway: Your best product decisions come from real user behavior, not theoretical frameworks. Sometimes the fastest way to validate an idea isn't a survey or interview—it's a working prototype.

🎨One of the most critical skills to build 0 to 1 products both at a startup or inside a large company is the art of prototyping.🎨 I call it art and less of science because it involves a lot of creativity to get to a working model with the least amount of time and cost and there is quite literally no standard repeatable process. Great product managers and product companies however have managed to create the right prototypes repeatedly rather scrappily by truly understanding what really matters the most. One of my favourite examples is of how Google Books was born. In 2002, Larry Page began wondering if it was possible to make every published book ever published searchable online. As the cofounder, Larry could have assigned a team of engineers to the problem and given them a nice budget. Instead he got a digital camera, rigged it to a tripod and set the contraption up on a table in his office. He pointed the camera at the table, turned on a metronome to pace his movement and starting snapping pictures whilst Marissa Mayer turned the page. Based on this crude prototype, they were able to estimate what it took to digitise a book. Google Books was born. There are numerous such examples. ✴️Airbnb was born over a design conference weekend when the founders put up their single airbed on a static website and got some guests validating their hypothesis. ✴️Netflix knew it was possible to rent dvds over mail because they could mail themselves a dvd and it came back to them intact. ✴️Ubers prototype was users would text their location and behind the scenes they used phone calls to despatch black town cars. ✴️The iPhone’s very first prototype was a phone module rigged to an iPod. If one were to abstract the key guiding philosophies that go into making great prototypes, it would be 👉 Not thinking of scale first. This is what kills most new products. Instead of validating value with a few people and then think of gradually scaling, big companies especially think scale first and inevitably spend a monstrous amount of time to build an unproven hypothesis at scale to immediately deliver business value. Inevitably when it fails, management has no further motivation to pursue given the huge cost already incurred. 👉Use existing infrastructure to quickly put together something to prove the concept works and users love it. Almost often no custom tech solutions are built but rather reusing existing solutions to make a scrappy contraption. 👉 A sense of urgency. Almost often these scrappy contraptions were put together in a very short time frame to quickly test the hypothesis. A word of caution here is that these guiding principles may not work for all industries say such as health care where human life is at stake. So be mindful of absolute non negotiables when defining your prototypes. #productmanagement #prototyping #productcraft #zerotoone

Uber PM: AI prototyping only goes so far. Is it possible to solve this? For most PMs still, eng is still the bottleneck. I completely sympathize with Nimisha! And don’t mean to call her out. I think there are 3 ways teams can solve this: 1. Modernizing code This one is really for engineering leaders to solve. You want to make the code base easy to vibe code with. > Are engineers using Claude Code and Devin? Or are they stuck with Github Copilot? > Can you build and deploy nearly instantly? Or does it take hours? > Is your code built with Tailwind and microservices? Or too big for AI context windows? 2. AI experimentation: You don’t always need to make huge backend changes. Set up with an Optimizely or Kameleoon where you can allows non-technical users to vibe experiment in a way that uses your design and code base. This unlocks front-end testing from the engineering dependency almost entirely, with the exception of a code review by the on-call engineer. 3. AI discovery: Not everything needs to go to production. Vibe code then send the prototype to Usertesring or Voicepanel. Get real human feedback in just a few hours. Resources: 1. Modernizing code: a. Meta example: https://lnkd.in/eeqvdbvV b. Tech guide: https://lnkd.in/eD6e6Wgt 2. AI experimentation a. Full guide: https://lnkd.in/e86mpjGR b. Tech review workflow: https://lnkd.in/etXnfc2C 3. AI discovery a. Teresa Torres: https://lnkd.in/e7Q6mMpc b. Detailed guide: https://lnkd.in/e9QrMEDw Want to stay up with this new way of working? Follow Aakash Gupta for daily tips.

Wow. I just built 3 mini-apps for PMs in under 10 minutes: an empathy mapper, a journey analyzer, and a competitive analysis tool with Opal (Google Labs). No PRD. No Figma. No tickets. Just an idea → an experience. Instead of debating documents, I’m now sharing working mini-apps with my team ask them "react to this, let’s refine it” I used Opal to prototype the vibe with an: -Empathy Mapper -User Journey Analyzer -Competitive Landscape Tool Each one took minutes. Each one was immediately shareable. Each one changed the conversation. Use Opal when: -You want to validate an idea before writing a PRD -You need a quick tool for a workshop or meeting -You want to make research or concepts visible -You want to better empathize about your user Think of Opal as your 10-minute lab. If it takes longer than that, move it to a full prototype — that’s where other AI prototyping tools come in. Tips for PMs adopting this workflow -Start tiny. Your first Opal app should take under ten minutes. That constraint keeps you focused on intent, not polish. -Think in verbs, not nouns. Prompts like “summarize feedback” or “visualize trends” produce far better prototypes than static descriptions. -Collaborate live. Invite designers, engineers, and stakeholders into the session. Watching the prototype evolve creates alignment faster than any meeting. -Reflect. After every prototype, note what worked. Each build sharpens your prompting instincts and your product intuition. 🔗 Guides + masterclass in the comments 👇

Can AI-powered coding tools finally close the gap between product ideas and reality? I had a fascinating conversation with Vanessa Lee, VP of Product at Shopify, about how AI is changing product validation. We can now prototype stuff that we would typically have to rely on the dev team being free. This changes everything about our feedback loops. Think about it: how many times have you written a detailed PRD, waited weeks for engineering capacity, only to realize the concept doesn't work once you see it in action? That cycle has been painfully slow, and it's cost us time, resources, and credibility. Now PMs can gut-check their ideas before involving the engineering team. You can prototype that complex user flow, test it with real users, and come to your developers with something concrete instead of abstract requirements. As Vanessa put it, "Our work is actually in communication and helping to make sure that everyone is building the right thing at the right time." When you can show instead of just tell, that communication becomes infinitely more effective. The caveat? Don't merge your prototypes into production without proper review. This is about validation, not deployment. How are you using AI tools to prototype your product ideas?

The usual thinking often goes, "We're changing the website/platform, so there's no point optimizing what we already have." This perspective, while common, can inadvertently equate experimentation solely with optimisation, potentially overlooking the enormous benefits of integrating a truly experimental approach into development and innovation. A replatforming or redesign project typically involves a complex decision-making and MoSCoW-style exercise centered around a set of features. It's often impossible to exactly replicate old features on a new platform, meaning crucial decisions must be made about what's essential and what might be dropped. Likewise, new platforms can introduce various potential new features, but are they truly worth the investment? These decisions can become complex, political, and increasingly stressful as deadlines loom. The risk is that choices are made based on internal influence rather than what will genuinely serve the customer, which is inherently difficult to guess. How can you better manage this process? How can you genuinely know what will deliver the best customer experience and commercial outcomes? EXPERIMENTATION! When done properly, experimentation (including but not limited to A/B testing) can fast-track this entire process and help you deliver a project that actually works. Consider starting by creating a comprehensive list of all feature disparities that need to be addressed. Then, establish an initial prioritization. Next, plan and run experiments for each consideration. Finally, assess the likely benefit. Some experiments are remarkably straightforward. If a new platform won't include a particular feature "out of the box," you could A/B test removing it from your existing site to understand its true importance. Others might be more challenging. If a new platform offers recommendations but at additional cost, you could conduct more rudimentary experiments on your existing site to test the core concept. Moreover, these features don't have to be front-end; the same process can be applied to backend operational features if you have the right expertise. Experimentation isn't just optimisation; it's a critical tool for informed innovation. #experimentation #cro #productmanagement #growth #digitalexperience #experimentationledgrowth #elg #growthexperimentation

I watched a client go from taking weeks to launch experiments... to literally a few hours. Here's how I helped them 👇 When I started working with this client, their experimentation program was slow. Every idea had to work its way through a maze of approvals, backlogs, and coordination across teams. By the time something launched, the window of opportunity had already closed. I wanted to help them move faster. MUCH faster. So I started introducing the things that set world-class experimentation cultures apart from everyone else. What, pray tell, might those things be? Well, I love explaining with a particular and very true story that goes around at a former employer: there was a copywriter who had an idea on her bike ride to work, came into the office (this is pre-pandemic, people), made the change in her CMS (which was connected to the experimentation platform), and launched it as a global experiment running before her first coffee. That’s the kind of speed I wanted my client to experience, and we made it happen. How? Sure, we tightened up their tech and data flows so experiments could run smoothly. But this was the minor point in all honesty. The real shifts came from bringing together a cross-functional team who had the skills to deliver autonomously, getting leadership backing for that team to take risks, and setting a clear and focussed goal for the team to rally behind. We removed unnecessary “approvals,” facilitated the essential conversations, created focus, and rewarded pace without compromising rigour (improving it, actually). The team became empowered to make their own decisions and built a culture that normalised risk-taking. The result was night and day. Just weeks earlier, ideas took months to get through approvals, builds, and launches. All before even monitoring, reporting, and decision making (if any) took place. Now? The team could go from an ideation session to launching quick wins in literally hours. And, I can tell you first hand, when a team experiences this shift from moving like a snail to sitting up front of a rocket ship, that acceleration brings creativity, confidence, and energy. The kind that spreads across teams and compounds. It’s infectious. Oh, and did I mention that they started getting more runs on the board too? 😉 So recap, what makes these cultures different? And what did we instil to help this team accelerate that fast? ✅ Leaders who empower and trust their teams. ✅ Teams with genuine ownership and motivation to create impact. ✅ A culture that celebrates learning, not just winning, and treats failure as fuel for improvement. That’s what lets someone move from an idea on a bike ride → to a global experiment before their first coffee. If you want to innovate and experiment at lightning speed, don’t just look at your tech. Look at your culture. Could your org handle that kind of speed? 👇

Happy Friday, this week in #learnwithmz, let's explore how AI is revolutionizing product prototyping, from idea to interactive mockup faster than ever. I’m delivering an internal talk on this topic for my team, and thought it would be valuable to share some highlights here as well. 𝐓𝐨𝐩 𝐀𝐈 𝐏𝐫𝐨𝐭𝐨𝐭𝐲𝐩𝐢𝐧𝐠 𝐓𝐨𝐨𝐥𝐬 𝐟𝐨𝐫 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐫𝐬 -Visily Transform text prompts, sketches, or screenshots into editable UI designs. 🔗 https://lnkd.in/gcerJweq - Uizard Generate wireframes and mockups instantly from text descriptions. 🔗 https://lnkd.in/grdSadcb - Microsoft 365 Copilot Prototype ideas directly within your workflow using Word, Excel, PowerPoint, and Teams. Great for early PRDs, visualizations, and cross-team brainstorming. 🔗 https://lnkd.in/gB2PNq9k - V0 by Vercel Create full-stack web apps from prompts, integrating frontend and backend. 🔗 https://v0.dev/ - Bolt Rapidly build and iterate on AI-driven product ideas. 🔗 https://boltai.co - Lovable Design and deploy AI-powered products with minimal coding. 🔗 https://lovable.so 𝐎𝐩𝐞𝐧-𝐒𝐨𝐮𝐫𝐜𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 - NodeTool: Build and automate AI workflows without code. 🔗 https://lnkd.in/gnnB_7UU - ReacType: Visualize and export React applications with drag-and-drop. 🔗 https://lnkd.in/geQbxbEC 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠𝐬 - Speed vs. Precision: AI tools are great accelerators, but manual polish is still needed for complex workflows and specific needs. - Experiment often: The space is evolving fast; test, learn, and share back. - Check before you use: Always check your company’s policies on tool usage, especially when working with sensitive product data or proprietary designs. 𝐅𝐮𝐫𝐭𝐡𝐞𝐫 𝐑𝐞𝐚𝐝𝐢𝐧𝐠 A Guide to AI Prototyping for Product Managers by Lenny Rachitsky and Colin Matthews 🔗 https://lnkd.in/ge6nbzcr Which AI prototyping tools are in your workflow or on your radar? Drop your experiences or recommendations below 👇 #AI #ProductManagement #Prototyping #AItools #learnwithmz

The PRD is becoming the sidecar. Not the main artifact. For years, product teams treated the PRD like the deliverable. Write the doc. Review the doc. Debate the doc. Align around the doc. But there is a hidden problem here: A PRD forces everyone to imagine the product in their own head. The designer imagines one flow. The engineer imagines another. Legal reads for risk. Leadership reads for strategy. Everyone says “aligned,” but they are often reacting to different products. A rough prototype fixes this. It gives the room one shared object to react to. “This button feels wrong” is more useful than three paragraphs explaining the intended interaction. “This flow breaks for international users” is more useful than a theoretical edge-case section buried on page 14. This is the shift Abhi Muchhal from OpenAI described: He was asked to write a PRD for a new platform investment. Twenty minutes in, he stopped writing and built the prototype instead. The conversation got better immediately. Not because the doc was useless. Because the prototype made the doc sharper. The new workflow looks more like this: 1) Build the rough version first Not polished. Not production-ready. Just enough to make the idea real. 2) Let people react to the actual thing Design, engineering, legal, safety, GTM, leadership. Same object. Same surface area. Same conversation. 3) Write the companion doc after Not a 20-page speculative PRD. A short FAQ: ↳ Why are we doing this? ↳ What must work for V1? ↳ What are the edge cases? ↳ What does success look like? ↳ What needs legal or safety review? That doc is usually better because it is grounded in something real. A PRD written before the prototype guesses at constraints. A companion doc written after the prototype answers the questions people actually have. This will not fit every org. Some products still need heavy upfront planning, compliance review, and architecture work. But for a lot of teams, the default is changing: The product is the output. The prototype is how you explain it. The doc is the companion that makes the thinking legible. The PM job used to start with the document. Increasingly, it starts with the prototype. Useful links: Modern AI PRDs: https://lnkd.in/eMu59p_z AI prototyping tutorial: https://lnkd.in/eJujDhBV Full Codex breakdown: https://lnkd.in/ggMA9E7t Abhi’s episode: https://lnkd.in/eMaEGejg I'm Shrey Shah & I teach AI assisted coding and agents.