Innovative Rapid Prototyping Techniques For Software Development

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.

TL;DR: Slack → GPT → Spec → Prototype 💡 Ever find your best ideas buried in a Slack thread—and the manual process of extracting them slowing you down? 💬 I ran into this often at Bumble Inc., so I built a custom GPT to help. Instead of manually recreating brainstorms or sketching early prototypes, I now take screenshots of the Slack discussion and send them to my GPT. It parses the chat, extracts the key ideas, and formats them into a mini spec—snack-size and ready to drop into Figma for prototyping. ⚡ This includes annotating who talked about which part of the idea, so when we go back to discuss things we can talk to that person and get even more context. It’s essentially “rapid prototyping,” but instead of sketching wireframes, I'm turning entire conversation threads into structured specs. Not only is it faster, but it also helps me spot conversation imbalances—like when we spent too long on one idea and barely touched another. I’m always looking for ways to pull more value out of Slack discussions—what’s working for you? 🤔 #ProductManagementAI

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.

From idea to prototype in hours, not weeks. That's been my recent experience experimenting with Lovable, and it's completely changed how I approach ideation and product thinking. Turning abstract ideas into clickable, interactive prototypes in no time means less talking about the concept, and more showing. In one recent build, the moment I shared the prototype, the conversation shifted from “What do you mean?” to “Is this how you see it?” That one shift sparked faster clarity, better feedback, and deeper alignment. No more endless meetings trying to describe what’s in everyone’s head. Here’s what I’ve learned along the way: 𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗮 𝗰𝗹𝗲𝗮𝗿 𝗼𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁. Even with powerful tools doing the heavy lifting, I start by organizing my thoughts on paper—with a clear outline, defined scope, and key user flows. The tool amplifies good product thinking, but it can't replace it. 𝟮. 𝗔𝗹𝗶𝗴𝗻 𝘆𝗼𝘂𝗿 𝘁𝗮𝘅𝗼𝗻𝗼𝗺𝘆 𝗮𝗻𝗱 𝗻𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗼𝗻 𝗲𝗮𝗿𝗹𝘆. This becomes incredibly clear when you're building a visual prototype. Getting your information architecture right from the start saves significant rework later. 𝟯. 𝗘𝗺𝗯𝗿𝗮𝗰𝗲 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗱𝗿𝗮𝗳𝘁 𝗳𝗼𝗿 𝗰𝗹𝗮𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸. Don't aim for perfection on the first build. Get something clickable in front of people quickly. The real insights come from watching others interact with your prototype, not from endless polishing. You can always go deeper and refine the prototype based on those initial insights. 𝟰. 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗹𝗼𝗰𝗮𝗹 𝗳𝗶𝗿𝘀𝘁. For initial builds, leverage local browser cache before connecting to databases or other external tools. It speeds things up considerably and keeps you agile. 𝟱. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗯𝗮𝘀𝗶𝗰𝘀 𝘀𝘁𝗶𝗹𝗹 𝗺𝗮𝘁𝘁𝗲𝗿. A crucial reminder: never store your LLM API keys in plain text, especially if your project is public or remixable. Low-code tools like Lovable don’t just speed up the work—they unlock momentum, clarity, and collaboration. These change the way we think, not just what we build. Been experimenting with Lovable, Replit, v0 dev, or similar tools? I’d love to hear your best practices. ------------------------- P.S Curious about prototyping, product thinking, or AI workflows? I host Sunday brainstorming sessions — DM me if you'd like to join the next one!

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.

Eric Xiao and I did a lightning lesson on how PMs can ship production features using AI coding agents. Eric walked through his complete system for going from single-line prompts to deployed features. Key takeaways: 🧠 Context Engineering Over Prompt Engineering - The bottleneck isn't writing better prompts, it's giving AI agents proper environmental setup. Eric creates "agents.md" files that document codebase architecture, testing patterns, and deployment processes - essentially onboarding AI agents like new engineers. ⚡ Tool Orchestration Strategy - Different AI coding tools excel at different functions rather than being general-purpose solutions. Codex for focused production PRs, Claude Code for UI development and planning, Cursor for direct editing, ASK feature for codebase exploration. 🔧 Multi-Layer Quality Assurance - Automated review processes can be more thorough than human code review while maintaining velocity. Claude Code for architecture, Cursor Bug Bot for debugging, CI/CD for testing - the system actually blocked Eric's test change as "inappropriate for production." 🔄 Inverted Development Process - Start with working prototypes instead of abstract planning. Traditional workflow: Idea → PRD → Design → Build. Eric's approach: Prompt → Working prototype → Iterate based on actual user interaction. Link to the recording here: https://lnkd.in/dM773wFW

If you still think building a startup prototype takes months, you’re probably not using the right tools. Last night, I went from zero to a working prototype in 30 minutes. No big team. No “stealth mode.” No months of planning. Just: 1️⃣ A real workflow bottleneck 2️⃣ AI-powered research 3️⃣ Instant wireframing & prototyping tools Here’s what actually happened (and how you can steal the playbook): Picked a real-world bottleneck: Instead of chasing “cool ideas,” I dug into the daily pain points of doctors in India—using ChatGPT & Gemini to synthesize thousands of words of research in minutes. Let AI play detective: Uploaded research docs to ChatGPT, got a prioritized list of the actual top bottlenecks. No assumptions, no wishful thinking. Prototyped instantly: Dropped the requirements into Lovable.dev, and in minutes had a clickable prototype ready to demo to real users. What did I learn? If you’re still spending weeks “validating” and “ideating,” you’re already behind. The right AI stack can take you from problem → insight → prototype before most people finish their deck. Ready to see how it works? Grab the full play-by-play PDF [Attached] Check the prototype link in the comments section. If you’re a founder still stuck on the whiteboard, try this workflow and break your own speed limits.

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