Rapid Prototyping Techniques

AI prototyping has changed what it means to be a PM, designer, and engineer in forward-thinking organizations. Here's how: The Old Way Here’s what most product development lifecycles look like: 1. Ideation Most teams barely prototype at the idea stage. A rare few exceptional designers and PMs do (~5%) 2. Planning Here, more teams use prototypes, but it still is an exception few (~15%), while sketches and mockups are much more common (>75%) 3. Discovery For many in feature factories, this step is skipped completely. But in more empowered companies, many teams would test prototypes in the discovery phase (~50%) 4. PM Handoff It's rare for PMs to include prototypes in their PRDs—often met with side-eyes from designers who prefer sketches or plain descriptions (5%). 5. Design Exploration This is the most common spot where prototypes come into the picture, and most product teams looking to reduce risk of big features prototype (~75%) 6. Engineering Start: Because many prototypes are not engineered, engineers would start from scratch with a final design. This has been steady for the past several years. The New Way But something is changing amongst forward-thinking teams. PMs are moving beyond documents and closer to the “bare metal” of the pixels that actually define a product. Prototypes have become the new way to communicate your ideas - at all stages: 1. Ideation They’re using prototypes to work out product problems. 2. Planning They’re pairing roadmap discussions, pre-PRD, with prototypes. 3. Discovery They’re putting these prototypes in front of real potential customers to explore solution spaces. 4. PM Handoff Then they’re attaching working prototypes to their PRDs—turning ideas into clickable clarity. 5. Design Exploration Designers are going from a PM-fidelity prototype to a design-level prototype (often in Figma) that fits into the design systems and goals of the team. 6. Engineering Start Engineers may even start with the latest prototype from a tool to get a headstart on their code. Then they’ll use AI coding tools like Cursor or Windsurf to get it ready for production. In other words: All 3 of PM, design, and engineering are now using these prototyping tools.

Most AI ideas die before they even get off the ground. Why? Because teams get stuck in endless debates instead of building something tangible. The best way to get leadership buy-in, align teams, and validate your AI concept? Prototyping. But here’s the secret—you don’t need to code to prototype AI effectively. Instead of diving into AI coding tools like Cursor or Replit, you can use no-code AI prototyping tools like Notion AI, UX Pilot, CustomGPTs, and Voiceflow to move even faster. In our latest AI Community Learning Series, Polly M Allen (Ex-Principal PM, Alexa AI) and Rupa Chaturvedi (AI UX Leader, ex-Amazon, Google, Uber) shared how to: ✅ Align teams faster with interactive AI prototypes (instead of lengthy PRDs) ✅ Use no-code tools to build AI-powered experiences—without writing a single line of code ✅ Pick the right AI use cases and avoid overcomplicating solutions Plus, they demoed how to build a Shopping AI Assistant live—showing exactly how to structure, test, and refine AI interactions in minutes. Curious how they did it? Full recap + session replay 👇 Have you built an AI prototype before? What worked (or didn’t)? Share your thoughts below! #ProductManagement #AI #Design #Prototyping

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?

Just watched a mind-blowing demo: Claire Vo built a ChatPRD feature in < 30 minutes end to end. This is what the future of product development looks like - and it's already here. Let me break down how this works... The tools used: - Chat PRD (for requirements) - V0 (for UI design) - Cursor (for implementation) - Devin (for code review) No more weeks of back-and-forth between teams. No more bottlenecks. Here's what's wild: Claire did everything herself (with AI assistance) - from PRD to implementation. No handoffs. No waiting. Just pure execution. This is the death of traditional role boundaries. The churn form included: - Feature usage feedback - Pricing assessment - Open comments - Email parameter tracking - Segment integration All spec'd out in minutes with Chat PRD. V0 took those requirements and turned them into a fully styled, mobile-friendly UI. The kicker? It matched existing design system perfectly based on screenshots. Better than previous manual implementation. Cursor handled the heavy lifting: - Generated multi-file code - Added Segment tracking - Set up event logging - Implemented form validation All while maintaining clean, production-ready code. Then Devin stepped in as the code reviewer: Pointed out needs for: - Better error handling - Loading states - Documentation improvements An AI doing thorough code review. Let that sink in. Outside of ChatPRD, Claire embodies this high agency in her day job by championing a "no lanes" culture in the ~200 person technology/product/design org she leads. PMs doing design work? Engineers writing PRDs? YES. Because AI makes it possible. When PMs can handle basic prototypes, guess what happens to designers, engineers, etc? They get elevated to higher-value work. Strategic thinking. Complex problems. Innovation. This isn't just about tools. It's about the future of collaboration: - Technical capabilities - Data analysis - Business acumen - Sales knowledge The age of generalists is here; but it will _elevate_ specialists so that they can operate at the top of their license. All while shipping much faster. Shipping faster and happier? Yes please. AI is the great enabler of this transformation. Traditional product development: 2 weeks for PRD 1 week for design 2 weeks for implementation 1 week for review New world with AI: 20 minutes total The implications are massive: - Faster iteration cycles - Lower coordination costs - Better products - Happier teams - More innovation The future belongs to generalists who can execute.

"The value of a prototype is in the insight it imparts, not the code" Prototyping lets us fail fast and cheap, or get the data to make a concrete decision on direction. It helps answer the question, "What happens if we try this?". Most significantly, prototyping provides us with the guardrails to safely and productively fail. Prototyping is the right tool if you have an idea to validate, a clear path to get feedback on, or a proposal requiring further data. It provides crucial insights to move forward. By creating a rough version of a feature or system you've been considering, you gain the flexibility to either discard the idea or fully commit to it. It's a skill that assists product and engineering teams in making pivotal business decisions. Whether it's a website, mobile app, or landing page, no matter what product you're working on, it's always essential to verify your design decisions before shipping them to the end-users. Some development teams delay the validation stage until they have a solution that is almost complete. But that's an extremely risky strategy. As we all know, the later we come across the problem, the more costly it becomes to fix it. Luckily, no matter what point you are in the design process, it is still possible to build and test a concrete image of your concept—a prototype. Consider an architect tasked with designing a grand building. Before laying the first stone, the architect crafts a miniature scale model, allowing them to visualize the end result, understand the project's complexities, and present their ideas convincingly to others. However, this model is far from being the final product; it's a means to an end. This principle applies just as aptly in the world of software development. A software prototype—whether it's a low-fidelity wireframe, a high-fidelity interactive model, or a simplified mock-up of a more complex system—is much like the architect's scale model. It's a visual, often interactive, model of the software that provides developers, stakeholders, and users with an early glimpse into the software's workings, long before the final product is ready. The prototype isn't about the code per se; the code is merely a tool used to create it. Instead, it is about gathering valuable insights, comprehending user needs, identifying functional requirements, validating technical feasibility, and discovering potential stumbling blocks that might arise during full-scale development. The prototype's strength lies in its capacity to provide these insights without necessitating a significant investment of time or resources. I'm a big fan of using prototypes in our work at Google. Their value is often high. Wrapping up... The aim of prototyping is not the prototype itself or its immediate output but the knowledge that comes from it. I wrote more on this topic in https://lnkd.in/gEEGFwJp #softwareengineering #programming #ux #design

I tried 10+ AI prototyping apps. Only one stood out. Here's why: Don't sleep on this tool. I tried the usual suspects (Lovable, Stitch, Make, Bolt, v0, etc.) But when I found Magic Patterns, I stopped looking. It had everything I needed for collaborative, AI-powered prototyping, especially in the early stages of the design process. Everyone’s debating which AI prototyping tool generates the best UI designs or code. Or they're showing off a random vibe coded app. But I think the real opportunity for product teams is being overlooked. Early-stage collaborative AI prototyping is where the magic happens. Fast exploration, shared context, real momentum. 3 Reasons why Magic Patterns excels at this: 1. Live AI prototyping with others = game changer Magic Patterns lets you invite people to a shared canvas. Review and interact with multiple prototypes in one view. Fork, remix, and build on ideas instantly. It’s multiplayer AI prototyping done right, perfect for my AI design sprint workshops. And perfect for product teams to rally around a problem and explore ideas. 2. Front-end focus, no backend noise You can explore flows and concepts fast, without getting distracted by databases or logic. Many of the hyped AI tools are focused on vibe coding complete apps. But for early-stage work you just need to quickly explore multiple ideas, iterate, get alignment, and test for feedback. For this purpose, Magic Patterns is exactly what I needed. 3. Thoughtful features that speed up your flow Magic Patterns is perfect for first-time AI prototypers. The beginner friendly interface and useful features like "Presets," "Inspiration," and "Polish", make it easy for anyone to experiment with purposeful ideas. Bonus Reason: Don't mistake Magic Patterns for a basic AI UI tool. There are advanced features and smart workflows I’ll show you that make this the most valuable tool I’ve added to my design process in years. I’m hosting a FREE live walkthrough next week where I’ll demo exactly how I use Magic Patterns inside my AI Design Sprint workshops, including best practices and the frameworks I’ve used in real sessions. This is a glimpse into how design, product, and engineering will work together in the AI era. Once you see it in action, you’ll want to run your next workshop this way. Come hang out. It’s going to be fun, useful, and maybe even a little magical. 🪄 Spots are limited. Drop “magic” in the comments or DM me to reserve your spot.

Rapid concept testing provides design metrics to make faster decisions. We recently wrapped up a 12-week design cycle with a customer, using Helio to collect 37,000 answers from 2000+ participants across 44 concept areas. Working with a stakeholder group of 10+ people, we created incredible velocity. The key to achieving this scale is understanding how we want the design to drive the business. We build confidence in the lift as a leading indicator by aligning concepts with a design metric. Multivariate testing provides us with a baseline for comparison. How does this work? First, we define the KPIs we want to measure on the live site. Then, we identify leading indicators that correlate with these KPIs to give us a sense of potential lift using design surveys. KPIs ↳ KPIs provide quantifiable measures of performance and impact, allowing for objective assessment and comparison over time. Leading Indicators ↳ Leading indicators offer early signals about the concept's potential success or issues, guiding necessary adjustments before full-scale implementation. A single survey test can't answer every design question, but using many questions—in this customer example, over 3,700—gives us strong signals. Here are a few design metrics we use to drive design decisions. Often, we combine these indicators to create a compelling signal. → Comprehension → Desirability → Viability → Usability → Sentiment → Response Time → Feeling → Reaction The future of design relies on fast research and easy access to audiences. #productdesign #productdiscovery #userresearch #uxresearch

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

Rapid testing is your secret weapon for making data-driven decisions fast. Unlike A/B testing, which can take weeks, rapid tests can deliver actionable insights in hours. This lean approach helps teams validate ideas, designs, and features quickly and iteratively. It's not about replacing A/B testing. It's about understanding if you're moving in the right direction before committing resources. Rapid testing speeds up results, limits politics in decision-making, and helps narrow down ideas efficiently. It's also budget-friendly and great for identifying potential issues early. But how do you choose the right rapid testing method? Task completion analysis measures success rates and time-on-task for specific user actions. First-click tests evaluate the intuitiveness of primary actions or information on a page. Tree testing focuses on how well users can navigate your site's structure. Sentiment analysis gauges user emotions and opinions about a product or experience. 5-second tests assess immediate impressions of designs or messages. Design surveys collect qualitative feedback on wireframes or mockups. The key is selecting the method that best aligns with your specific goals and questions. By leveraging rapid testing, you can de-risk decisions and innovate faster. It's not about replacing thorough research. It's about getting quick, directional data to inform your next steps. So before you invest heavily in that new feature or redesign, consider running a rapid test. It might just save you from a costly misstep and point you towards a more successful solution.

Product development in 2024 - the old way: • Design low-fi wireframes to align on structure • Create pixel-perfect Figma mockups • Socialize designs with stakeholders • Wait weeks for engineering capacity to build • Build core functionality first • Push "nice-to-have" animations to v2 • Ship v1 without thoughtful interactions • Iterate based on limited feedback • Repeat the cycle for 3-6 months Product development in 2025: • Quickly prototype in code with AI tools like Bolt • Generate functional prototypes in hours, not days • Deploy to real URLs for immediate testing • Add analytics to track actual usage patterns • Test with users while still in development • Designers directly create interaction details • Engineers implement interaction details by copying working code • Ship v1 with thoughtful animations and transitions • Iterate rapidly based on both qualitative and quantitative data • Implement improvements within days Last week, we hosted William Newton from Amplitude to share how this shift is fundamentally changing their product development approach. "I made those interaction details myself. I made those components myself, and I sent them to my engineer and he copied and pasted them in." Features that would have been pushed to "future versions" are now included in initial releases. Loading animations, transition states, and micro-interactions that improve user confidence—all shipped in v1. This approach doesn't eliminate the need for thoughtful design and engineering. Instead, it changes the order of operations: - Traditional process: Perfect the design → Build the code → Ship → Learn - Emerging process: Prototype in code → Learn while building → Ship with polish → Continue learning The limiting factor is shifting from technical implementation to your taste and judgment about what makes a great experience. When designers and PMs can participate directly in the creation process using the actual medium (code), they make different—often better—decisions about what truly matters.