Functional Prototype Development

Most PMs are still writing 15-page PRDs that developers skim and designers ignore. Meanwhile Nadav Abrahami spent 20 years building Wix into a $4B company, then left with 30 of his best engineers to solve the problem he watched PMs struggle with the entire time: you can describe a feature in a thousand words, or you can just build it in 10 minutes. The stat that should wake people up: MIT found 95% of enterprise AI projects fail to reach production. The prototypes break down before they ship. The gap between "cool demo" and "something that works" is where most teams die. What Nadav explains in this episode is the workflow that closes that gap (https://lnkd.in/gW8AXKXG). His team at Wix used to assign three developers for weeks to build functional prototypes for major features. Now every single feature goes through AI prototyping before a line of production code gets written. The time cost went from weeks to minutes. The real insight though is his framing of where PMs go wrong. They treat AI prototyping like vibe coding, dump a massive prompt, and hope. His approach: discuss with the AI first. Ask it "how do you understand this?" the same way you'd sanity-check with a developer. Because anything that can be misinterpreted will statistically be misinterpreted, and unlike a developer, the AI won't tell you your spec makes no sense. One line from the conversation that stuck: "PMs just got a huge get out of no developers jail card." The prototype becomes the spec. The PRD covers edge cases. Together they should leave zero questions for the engineering team. Three years from now, PMs who can't prototype are going to be like designers who can't use Figma in 2015. Technically still employable. Practically falling behind every sprint.

Prototyping is how ideas turn into evidence. It surface hidden assumptions, generate better stakeholder conversations, test specific hypotheses, reveal unforeseen interactions, and give you a concrete artifact to evaluate before code or tooling locks you in. Use low fidelity sketches and storyboards when you need speed and divergent thinking. They help teams externalize ideas, reason about user goals, and map flows before pixels appear. They are deliberately rough to avoid premature polish. Move to click through wireframes in Figma when the question is structure and navigation. Validate information architecture, menu depth, labeling, and path efficiency while changes are still cheap. When the feel of interaction matters, use interactive digital prototypes to evaluate micro interactions, timing, and visual polish. Treat them as validation instruments, not trophies. Plan change criteria up front so attachment to a pretty artifact does not silence real feedback. Some questions require real performance and materials. Coded prototypes and functional hardware mockups tell you about latency, reliability, durability, ergonomics, and safety. In medical devices and other regulated domains, high fidelity functional and contextual testing is expected for Human Factors validation. Not every question lives on screens. Experience prototyping and bodystorming put bodies in space to surface constraints that lab tasks miss. Acting out a shared autonomous ride with props reveals comfort, cue timing, and social norms. Wearing a telehealth mockup for a week exposes stigma, routine friction, and alert patterns that actually fit domestic life. Before building intelligence, simulate it. Wizard of Oz studies let a hidden human drive system responses while participants believe the system is autonomous. You learn vocabulary, trust dynamics, acceptable latency, and recovery strategies without heavy engineering. AI of Oz replaces the human with a large language model so you can study conversational realism early. Manage risks like model bias, hallucinations, and outages with guardrails and logging so findings remain trustworthy. Strategic prototypes also matter. Provotypes and research through design artifacts challenge assumptions, surface values, and force early conversations about privacy, power, and trade offs that slides tend to dodge.

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.

What if I told you we took a raw idea - and turned it into a fully functional, AI-powered, user-loved feature in under 2 weeks? No roadmap. No sprint planning. Just a clear idea, a focused team, and smart use of AI at every stage. This is the behind-the-scenes of how we built and launched Quarter Review at Shikho - a new experience that helps students reflect on their learning journey, celebrate wins, and stay inspired. It started with a thoughtful suggestion from Novera: “What if we gave users a recap of their learning? Something they could share and feel proud of?” Here’s how that idea turned into a live feature - and how AI played a central role throughout: 1. From idea to structure (ChatGPT) ChatGPT helped move from raw ideas to structured concepts. With context - user data schema, platform behavior, product goals - it outlined what to show, why it matters, and how to present it. Screenshot 1: First working wireframe. Visually basic, but aligned the team in direction. 2. From concept to code (Cursor) Cursor accelerated prototyping - but reminded us AI needs precise instructions. After a few false starts, we built habits around tighter prompting and frequent version tracking via git. Lesson: Speed isn’t frictionless - but friction teaches better prompting. 3. From function to UX (UX Pilot + ChatGPT) The prototype needed visual polish. Sufian bhai introduced UX Pilot. Using ChatGPT to craft detailed, tone-aware prompts, we generated clean, user-friendly UX screens with animated graphs. Screenshot 2: Final wireframe - polished and ready for handoff. 4. Personalized copy (ChatGPT) Every screen includes tailored, motivational text - generated with ChatGPT, but not out of the box. We trained it with our tone, push notification style, and examples to ensure the copy felt like “us.” 5. From prototype to product (Team effort) Once validated, Angona led the launch as Product Manager. Engineering, QA, BI, and data engineering delivered the feature fast and then our Marketing team launched a co-ordinated campaign to take the feature to our users! Screenshot 3 & 4: Student posts from our Facebook community - some celebrating, others motivated to improve. Exactly the kind of engagement we hoped for. This project moved fast - not by cutting corners, but by being intentional. Each tool - ChatGPT, Cursor, UX Pilot - was used where it fit best. The process wasn’t just AI-assisted. It was AI-aware. Key takeaways for building with AI: ✅ Speed comes from structure, not shortcuts. ✅ Great results rarely come from one prompt - iteration matters. ✅ Context is everything - especially for UX and copy. There’s a lot of talk about AI accelerating work - this was one case where it truly did. #Shikho #EdTech #Startups #QuarterInReview #BuiltWithAI #ChatGPT #Cursor #UXPilot #LearningJourney

I'm rolling out a new process for one of my client projects. No feature makes it to the roadmap without a 1-pager and prototype. Not a write-up. Not a Slack thread. Not a "let's discuss this in the next planning meeting." A one-pager that explains the problem and a clickable prototype that shows the solution. This came from watching the same pattern play out across multiple startups and even at Google: Someone has an idea. It sounds good in the meeting. It gets added to the roadmap. Engineering starts building. Then halfway through everyone realizes it doesn't actually work. Words are too abstract. Static mocks hide the hard questions. But a prototype? You can't fake your way through a prototype. If you can build a working flow in Figma, you've thought through the journey. You know where users enter, what they do, where they might get stuck, and what happens when they're done. And if you can't build the prototype? You probably don't understand the product and feature well enough to ship it. This does a few things: - It kills half-baked ideas before they waste engineering time. - It gets stakeholder buy-in faster because they can actually see and interact with the thing. - It forces the person pitching the idea to do the hard work of thinking through the details upfront. - The whole team stays aligned because everyone can click through the same flow and ask questions before priorities get locked in. I've seen too many features die in development because nobody understood the edges. This process stops that.

Single prototypes tell you nothing about system reliability. Modularity is the secret key you're missing. When we built the multi-function demonstrator for Hyundai Cradle, we created a series of modular prototypes. Each targeted at validating specific performance vectors. → Thermal modules tested for uniformity and delta-T across surfaces → Touch and switch modules evaluated for actuation force versus signal-to-noise ratio → Pressure sensing modules designed to maintain accuracy under cyclic compression and lateral shear Key variables we isolated included: → Material stack-up compression profiles during environmental cycling → UV adhesive bond stability across operational temperature bands (-40°C to +85°C) → Electrical resistance drift under flexural fatigue testing (bend radius <5mm, 10,000+ cycles) By modularizing early, we could: → Identify failure modes before scaling → Fine-tune adhesives, conductors, and substrates independently → Model manufacturing tolerances with real data, not assumptions In hardware, scalable design isn’t about the first build. It’s about how you architect your prototyping process.

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!

Industrial Design Lab Note # 1 Material: TPU Goal vs Reality: Functional prototype journey using my Bambu Lab H2S Things I learned this week: · Nozzle size and temperature matter more than you think. Too small, and flexible filament decides it would rather live inside the hot end. · Extrusion diameter needs to be dialed in almost perfectly or the print turns into a rubber noodle sculpture. · Print speed must slow way down. TPU has zero interest in keeping up with your normal settings. · Cooling and ventilation change everything. Too much and layers separate. Too little and the whole part gets…melty. Ask me how I created an exhaust set up that can be used for both my laser and 3D printer. Flexible materials are great for functional prototypes. They are also extremely good at reminding you that your printer setup, slicer settings, and general optimism are all slightly wrong. Eventually you get it dialed in. Then you open a new spool and start over. Expect and plan for the unexpected. Oh and no I am not about to pay 1K for a single roll of material.  🤣

When a startup comes to us and says "we want to build a connected device," the first thing we do is disappoint them. We say no to writing code for at least two weeks. Founders hate this and it's pretty normal • You want to see code.  • You want to see a blinking LED. But trust me on this one... The process we follow at Metadesk Global before a single line of firmware gets written is tried-and-tested at least 500 times: Week 1: Requirements analysis We break "I want a smart device" into concrete blocks. What sensors?  What connectivity?  What power source?  What form factor?  What cloud platform?  What user interface? Most founders haven't thought through half of these. Week 2: Hardware-software interface definition We write a document that specifies every pin mapping, every communication protocol between components, every power state, every failure mode. This document is the contract between hardware and firmware. Week 3: Component selection and risk assessment We pick the MCU, sensors, connectivity modules, then catalog what could go wrong. • Is the chip available?  • Does the antenna need certification? • Is the module approaching end-of-life? Week 4: Architecture review and prototype plan We present the full system architecture, walk through the tradeoffs, and agree on a prototype timeline. Only then do we start building. Adding 4 weeks to the front of every project sounds expensive. But in three years of running this process... not a single project that followed it missed its ship date by more than two weeks. What does the first month of your product development process look like for you?

Customer discovery via functional prototypes + PostHog is night & day better than the old school way of asking for feedback on Figma mockups. Here's why: I get to observe actual user behavior instead of asking the user to guess how they might use my product. My favorite example of why this matters comes from a Sony Walkman user study. They asked a bunch of people what they thought about a yellow walkman and they said "so sporty! not boring like the black one!". And yet, when they were given the opportunity to take a walkman home after the study, everyone picked the black one. We learned a lot more from user behavior than we did expressed preferences. Here's my setup for now observing user behavior from prototypes: 1. Create a functional prototype in your favorite prototyping tool (Bolt, Lovable, Reforge Build, Magic Patterns, Claude Code) 2. Ask the prototyping tool to integrate PostHog analytics 3. Ask the prototyping tool to instrument key user actions in PostHog Then you get all of these ways of observing actual behavior: - DAUs \ WAUs \ retention curves - I can actually see if people come back and use my prototype instead of taking their word for it - Action metrics dashboards - I can see what actions people are taking vs not - Post-usage survey - I can add a built-in pop-up survey to ask the user a question about the experience after they have engaged with the prototype - Session replays - I can see exactly where people are clicking and how they are using the product to identify usability issues - Heatmaps - I can see what part of my design is working across all sessions I'd never go back to testing with just a mockup after this.