Rapid Prototyping Techniques

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.

Prototypes aren't for testing your product. They're for testing your assumptions. Most teams get this backward, and it costs them weeks of wasted effort and a product nobody wants. A prototype isn't a tiny product; it's a medium for learning. It's a tool designed to ask a specific question and test a core assumption with the right audience. An unintentionally designed prototype is a flawed input, and even with advanced teams and tools, flawed inputs only amplify flaws. The true power of a prototype isn't in its polish, but in the intentional "message" it sends. To unlock this power and truly accelerate collective learning across your organization, you must design with intent: ✺ Low-Fidelity Prototypes: These are for asking foundational, "Does this even solve the right problem?" questions. They signal that everything is up for debate. The intentional message is: "Let's explore the idea, not the pixels." ✺ Medium-Fidelity Prototypes: Use these to test core user flows and information architecture. The intentional message is: "Is this journey intuitive?" By keeping them a little rough, you prevent stakeholders from getting fixated on visual design. ✺ High-Fidelity Prototypes: Reserve these for the final stages to test things like micro-interactions, brand consistency, or subtle emotional responses. The intentional message is: "We're almost there. What are we missing?" This is how you turn prototyping from a simple task into a strategic lever for change and Team Learning. It ensures your team isn't just building things, but is learning together and making better decisions about what to build and why. It's how you break down silos and create a "Holding Environment" for generative dialogue. What's a time you intentionally used a low-fidelity prototype to prevent a high-stakes meeting from spiraling? Let’s discuss in the comments below. #ProductDesign #SystemsThinking #StrategicDesign #UXStrategy #DesignLeadership #ComplexSystems #TeamLearning #Prototyping #OrganizationalDesign #Innovation

Talk less. Prototype faster. The best teams don’t discuss ideas endlessly; they just build them. But how do you get the right prototype fast enough? Most new product initiatives are not about creating a new product. They're about improving existing ones. In other words, they already have a product, customers, and a design language. The machine is slow, perhaps rusty, but it has worked for ages now. Any attempts to improve the process usually failed or gave barely any noticeable improvement. However, this is where the AI comes in and why I’m genuinely impressed with Reforge Build, which has now been launched in beta! It’s an AI prototyping tool made for product teams, not solo builders. It starts where your product already is and accelerates what comes next. Don't take my word for it, try it yourself: Check out Reforge Build and explore what’s possible with AI that actually understands your product: https://lnkd.in/duh4YC_H But why did it impress me? 1) Looks like your product Upload a screenshot or connect to Figma. Reforge Build instantly matches your real design system: colors, fonts, spacing, everything. No endless cleanup. No imagination is needed when painting a vision of a future successful product to the stakeholders. 2) Understanding the context Add your product data, strategy docs, and customer insights. Build the prototypes using your actual tiers, features, and messaging. This won't be just a rough draft, but something your actual design team could have presented to you after weeks of work. 3) Plans before it generates Instead of vague prompts, you define user needs, metrics, and layout priorities. AI creates a plan before generating, so the first version is already close to your vision. After all, you need a workable prototype, not an AI slop wannabe! 4) Explores options, not just outputs This REALLY left me with my jaw on the floor: Reforge Build generates multiple design directions, compares them side by side, and mixes the best ideas. I can only imagine this is the experience of a Product Manager with multiple design teams ready to work on a single project... 5) Works like a team tool, not a solo hack Comment, remix, reuse templates, so your second iteration takes minutes, not hours. Nobody's perfect, not even your AI teammate, but every teammate gets better with proper feedback! Impressive, isn't it? Would such an AI prototype tool speed up your new feature's go-to-market time? Let me know in the comments! #productmanagement #ai #ux

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 👇

🎨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

We built a full web app at a hackathon in under 48 hours. Not just a toy. A full MVP we're now testing in the market. No engineers. Just a product team and AI. I was gobsmacked by what we pulled off. We used Python and TypeScript — generated entirely through prompting. It felt like pair programming with an intern who never sleeps and sometimes forgets what you said ten minutes ago. Here’s what worked: 🗺️ 𝗣𝗹𝗮𝗻 𝗳𝗶𝗿𝘀𝘁 AI writes fast, but it won't fix a broken foundation. 🎯 𝗕𝗲 𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 Vague prompts = duplicated logic, broken flows, and three surprise databases. ⏱️ 𝗖𝗼𝗺𝗺𝗶𝘁 𝘀𝗺𝗮𝗹𝗹, 𝗰𝗼𝗺𝗺𝗶𝘁 𝗼𝗳𝘁𝗲𝗻 AI moves fast. Frequent checkpoints keep the repo sane. 👥 𝗣𝗮𝗶𝗿 𝗲𝗮𝗿𝗹𝘆 We started together to create a shared foundation. Async was smoother after that. 🔍 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝘀𝘁𝗶𝗹𝗹 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 When the AI goes off-script, your technical instincts are still key. And five minutes with a good engineer can save you five hours of pain. This isn’t about replacing engineers. It’s about collapsing the idea-to-prototype loop. You still need engineers to make things stable, secure and scalable. But you don't need to wait weeks to test an idea. Product managers know how to experiment. What’s new is how cheap and fast it’s become to build. Anyone else tried building this way? I would love to hear what's worked (or blown up) for you. #ProductDevelopment #VibeCoding #NoCodePlus #ProductManagement

Triumph, a global lingerie powerhouse, had a problem. 🚨 Over 20,000 physical samples were being produced annually. 💸 Costs were spiralling. 💸 Sustainability goals were slipping. 🔄 And teams were stuck in traditional processes that no longer made sense. They knew 3D could be a game-changer. But knowing and implementing are two different things. 👉🏾𝗧𝗲𝗮𝗺𝘀 𝘄𝗲𝗿𝗲𝗻’𝘁 𝗮𝗹𝗶𝗴𝗻𝗲𝗱—Designers were excited, but product development teams questioned accuracy. 👉🏾𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝘄𝗮𝘀𝗻’𝘁 𝗰𝗼𝗻𝘃𝗶𝗻𝗰𝗲𝗱—Would 3D compromise quality? Would suppliers adapt? 👉🏾 𝗥𝗲𝗴𝗶𝗼𝗻𝘀 𝗺𝗼𝘃𝗲𝗱 𝗮𝘁 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝘀𝗽𝗲𝗲𝗱𝘀—Europe relied heavily on physical samples, while Asia took a more cautious approach. 𝗛𝗼𝘄 INHOUSE 𝘄𝗼𝗿𝗸𝗲𝗱 𝘄𝗶𝘁𝗵 𝗧𝗿𝗶𝘂𝗺𝗽𝗵 The Process: 1️⃣ 𝗣𝗶𝗹𝗼𝘁 𝗙𝗶𝗿𝘀𝘁, 𝗣𝗿𝗼𝘃𝗲 𝗥𝗢𝗜 We tested 3D on Triumph’s best-selling collections. The results? Faster approvals, fewer samples, and clear cost savings. 2️⃣ 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗖𝗮𝘀𝗲 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗖𝗼𝘂𝗹𝗱𝗻’𝘁 𝗜𝗴𝗻𝗼𝗿𝗲 We showed leaders that beyond cutting costs, 3D improved speed to market, sustainability, and decision-making. 3️⃣ 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 & 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Because transformation doesn’t happen overnight, we supported it to keep momentum going. 𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: ✅ 85% reduction in physical samples—huge cost savings. ✅ Less waste, more sustainability—cut excess fabric and dyes. ✅ Designers gained creative freedom—less time on manual work, more on innovation. ✅ Faster decision-making—3D samples allowed for quicker approvals across teams. 𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝘀𝗵𝗶𝗳𝘁? 𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲. As Tamsin Artus Smith, Triumph’s Global Head of Future Product Concept, put it: “The biggest thing we gained was knowledge—knowledge that gave us the confidence to navigate leadership, secure investment, and keep pushing forward. We didn’t just learn 3D; we learned how to lead in 3D.” And the best part? They’re just getting started! 🚀 What’s holding your business back from adopting 3D? Share your questions and concerns in the comments! ** Use the link https://bit.ly/40XCJDv to watch the full case study ** #DigitalTransformation #Sustainability #Leadership #FashionInnovation #INHOUSE

🚀 AI is revolutionizing prototyping—making it faster, smarter, and more sustainable. From my early work with Sony’s AIBO to advising AI startups today, I’ve seen firsthand how AI-generated prototypes are transforming industries. What once took months of manual iteration is now done in days with AI-driven design, 3D printing optimizations, and digital twins. 🌍 The impact? Lower costs, reduced waste, and smarter material usage. Companies like Airbus, BMW, and Adidas are already leveraging AI to cut material waste by up to 50% and reduce costs by over 70%. Startups can now test and refine products virtually before manufacturing a single physical model. This is not just about efficiency—it’s about sustainable innovation. AI is reshaping how we build, test, and bring ideas to life. Those who embrace it now will gain a massive competitive edge. Read my latest article on the rise of AI-generated prototypes and how they are changing the game 👇 #AI #Innovation #Sustainability #Prototyping #3DPrinting #DigitalTransformation #AIStartups #FutureOfTech

If your prototypes still need €1 million budgets and eighteen-month timelines, the real bottleneck isn’t talent or tooling. It’s the data loop guiding every next step. Traditional “agile” used to mean a dozen steering meetings and incremental checkpoints that leave competitors ample time to catch up. FAST compresses that journey by turning insight into action on the same day it lands. • Scientists, lawyers, and strategists share a unified data graph that keeps everyone working from one live canvas. • Agentic AI curates new signals in real time and flags dead ends early while highlighting the highest-impact pathways. • Engineers spin proofs in days, building while leadership still feels the urgency behind the question. A Fortune-100 pharma group replaced a stalled €300 million AI programme with a FAST-guided prototype in six weeks, freeing €150 million for bolder bets and proving that momentum can outpace headcount. Your calendar does not have to dictate your velocity. #RapidPrototyping #Innovation #RND #Future

You know how it usually goes: PM drops a massive PRD... → designer stares at it forever → first prototype takes days to appear. By then, the team’s energy? Already fading. I've noticed this in my workflow. I wasted too much time just getting to the 𝘧𝘪𝘳𝘴𝘵 𝘥𝘳𝘢𝘧𝘵. And honestly, it hurts. The magic of product design happens when the team bounces off a quick prototype, not when everyone’s waiting for one. I've tested a bunch of AI tools to improve my workflow. Here's what I ended up with, in short: 👉 I take the PRD and throw it into ChatGPT. Ask it to turn it into a prompt for Figma Make. 👉 Paste the prompt into Figma Make → boom, instant draft screens. 👉 Now I’ve got something to react to, refine, and share with the team 𝘵𝘩𝘦 𝘴𝘢𝘮𝘦 𝘥𝘢𝘺. Ideation on steroids. A few pro-tips I’ve learned: • Don’t bite off the whole product. Start with 𝘰𝘯𝘦 𝘶𝘴𝘦𝘳 𝘧𝘭𝘰𝘸. • Phrase prompts as user stories (“As a user, I want to…”) → works way better. • Never treat the AI draft as final. It's always a starting point. Some may argue, but I'm not replacing creativity. I get rid of "blank canvas" stage and have more time for ideation. Try Figma Make yourself: https://lnkd.in/g3KJx4Gw ✌️ P.S. ↓ I've prepared a short guide with a bit more details on my process. Hope it helps. P.P.S. Do you use AI tools in your design workflow? #FigmaPartner #Figma