Prototyping for User Validation

20 years of building software taught me this: 1 killer prototype > 10 PowerPoints. Everyone talks about validating ideas. Nobody explains how to prototype fast without burning a week. Here’s the simplest way to build a prototype without burning a sprint: 1. Map the core flow Not the whole product. Just the path from “start” → “success.” Most teams overbuild here and drown. 2. Wire real behavior Fake buttons and placeholder data hide the problems. Move real data. Trigger real state changes. 3. Run the flow like a user Click every button. Fill every field. Refresh the page. Try to break it. This is where the real requirements show up. 4. Fix the first 5 issues You’re building direction, not perfection. A prototype only needs to work once end-to-end. 5. Put it in front of someone Stakeholders. Users. Your team. A working flow sparks better decisions than any deck. And here’s where Anything Max came in handy: Instead of wiring everything myself, I described the flow, and Max built the UI, the routes, the logic, the DB model, the emails, and the tests. Then it did the part nobody wants to do: - Opened the app in a real browser - Logged in - Clicked through the flow - Found what broke - Fixed it - Ran it again If you want faster validation without blowing up your roadmap, use tools that help you prototype, not plan. I put together a guide on building a working prototype using Anything. Comment "Anything" and I'll send it over.

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.

Stop trying to perfect features before development. Most junior product managers fall into two traps: analysis paralysis or rushing straight into development without validation. Both are expensive mistakes. After years of working with teams, there are actually two gates you need to think about, not one. Gate 1: Validation → Is it worth building? This is about de-risking the why and what, not the how. You need evidence that users want this solution, but you'll never get 100% confidence. I assess based on cost risk, technical complexity, user impact, and business implications. For a high-risk feature? 60% confidence might be enough to move forward. For lower stakes? Even 50-50 could work. Gate 2: Specification → Is it defined enough for developers to start? They need to understand what they're building without guessing. That means clear user flows, data requirements, and integration points. But you don't need pixel-perfect designs or every edge case solved upfront. The key is collaboration. On my team at Product Institute, developers and I go back and forth off prototypes as we build. That's what keeps us agile. You're not ready when developers ask the same clarifying questions repeatedly, or debate fundamental assumptions instead of implementation details. How do you balance definition with speed in your team?

When your product team skips user research (and just builds fast with AI)... 🤡 AI lets you ship in days, not weeks. But without user research, speed just gets you to the wrong place faster. A product leader was about to ship a feature last week. AI-assisted code. Cloud deployment ready. Built in 3 days. Then someone from customer success flagged something: "This solves a problem our users stopped complaining about 6 months ago." One conversation. Saved weeks of wasted effort. We can build faster than ever. AI tools, cloud code, vibe coding—the speed is real. But speed without direction is just efficient wandering. The real question isn't "Can we ship this?" It's "Should we?" That's where user research becomes non-negotiable. Here are 5 ways to quickly validate ideas before you ship: 1. Run a 5-second test on your concept (does it communicate what you think it does?) 2. Send a quick survey to your target segment (10 questions max) 3. Do 3-5 unmoderated usability tests on a prototype 4. Interview 5 users who match your ICP (even 15-min calls work) 5. Card sort your information architecture before building navigation None of these take weeks. Most can be done in days. Tools like Lyssna make this even faster. You can recruit from 690K+ vetted participants, run moderated or unmoderated tests, and get results before your sprint ends. Speed is a competitive advantage. But only when you're building the right thing. What's your go-to method for quick validation? PS. Save this for the next time you're tempted to ship something fast without validating it first. Future you will thank you.

I posted this image last month and a lot of people asked for a breakdown — not the theory, but how each stage actually works in a real project. Here’s the reminder this visual was meant to give: Understand → Ideate → Test → Implement is not a straight line. It’s a loop. You return to previous stages every time new data proves you wrong. Example from my own work: I was designing a dashboard for a SaaS product. The UI looked polished and was already “ready for handoff,” until usability testing showed that 4 out of 6 users couldn’t correctly interpret the main metric. So we had to loop back: → Understand: clarify user mental model → Ideate: restructure hierarchy + labels → Test: validate again with a quick prototype → Implement: only then ship the updated version The design didn’t change visually — the clarity did. Task success rate went from 42% to 91%. That’s real UX. Not a clean slide with arrows — but constant informed rewinding. A few things people underestimate in real projects: • “Understand” is not only interviews — it’s business goals, constraints, and success criteria • “Ideate” is not Dribbble-style wireframes — it’s structured problem solving • “Test” is not just moderated sessions — analytics, heatmaps, and field feedback count too • “Implement” doesn’t end at handoff — onboarding, content, states, and accessibility are still design The process doesn’t fail. What fails is expecting it to work in one direction. What is your take on this? #uxdesign #productdesign #designprocess #userexperience #uxresearch #uidesign #uxworkflow #designthinking #uxstrategy #usabilitytesting #saasdesign #uxcasestudy

What should I focus on? RAG, fine-tune or build my own LLM. Erm, none 🫣 At a recent conference, I was asked this question, and my answer surprised some folks, mainly because I focused on talking about solving user problems vs being tech-first - jotting my thoughts down below: 1 - Understand your customers/users needs  What are your customers/users trying to solve? Why is that a problem? What are they doing today? Why is that challenging?  Then working backwards... 2 - What are the tech/data needs? What are the analytic needs?  Where do those come from? How does the customer/user do this today? What challenges do they face with tech/data/analytics? Etc. 3 - What processes does the customer/user follow?  What interactions do they have with others? What rules and processes are a must? Etc. Now, before you build any real solution... 4 - Prototype a solution - and test it with your customers/users  - Will it solve their needs? - Will it do that in a frictionless way? - Will it improve their lives? Notice that I said prototype. You do not need to build a working solution — just something quick to validate your ideas. You may also have to iterate here a few times to figure out what works for your customers/users. If you get this far, and you believe you have a validated idea AND believe it will solve customers/user needs, then we can ask: 5 - Do we really need GenAI? 🤔 And, does it need some special domain knowledge? 9 times out of 10, we probably don't need a GenAI solution 🫣, but for discussion's sake, suppose we believe a GenAI-powered solution is right 🤷♂️….Now we can talk about RAG - BUT, again, build something lightweight and test it with your customers/users, answering  - Will it solve their needs? - Will it do that in a frictionless way? - Will it improve their lives? If your solution has legs…then you can think about improving the engine. Since you already know you have a working solution that solves user needs, you can now think about fine-tuning - or using a different/better LLM engine. If a tool vendor comes to you with tool X or Y, and straight away starts talking about fine-tuning LLMs, GPUs, Agentic AI, or building your own LLM but does not understand your user/customer problems, smile politely, walk backwards and run…. Good luck 🙏 #designthinking #genai

I gave up 2 hours of my weekend to test Claude Design. Here’s what I found as a PM. Not to review features. I wanted to answer one question: Can I prototype a real product flow without pulling a designer in? I picked a real problem: an internal moderation dashboard I’d been trying to get on the roadmap for weeks. No Figma file. No design brief. Just a prompt. 15 minutes later, I had a multi-screen flow with our brand tokens, a review queue, and an approval workflow. Not pixel-perfect. But clear enough to put in front of a VP and unstick the conversation. That’s the real signal. Not “AI replaces designers.” It’s “PM unblocks herself.” What actually works ✅ Idea → reviewable prototype in minutes, not days ✅ Connects to codebase/Figma to auto-apply brand tokens → outputs stop looking generic ✅ Live parameter sliders per design → tweak spacing, tone, layout without re-prompting What to watch out for ⚠️ Token economics are real → complex flows burn Pro allowances fast. Batch your inline edits instead of chaining prompts. ⚠️ No backend/state → it’s a high-fidelity wireframe, not a shippable product ⚠️ Vague prompts = generic output. Context is the multiplier. Where I would actually use this as a PM • Unblocking early stakeholder conversations before design bandwidth opens • Concept validation with users before committing to a sprint • Internal tools nobody wants to prioritize → show, don’t tell What Figma should watch - Not pixel-perfect editing. They’ll always win there. - It’s the upstream layer: exploration, synthesis, early alignment. If Claude Design owns that surface, Figma becomes a finishing tool, not a thinking tool. That’s a workflow shift, not a threat. My honest take Claude Design won’t replace your design team. But it will compress the time between “I have an idea” and “Let’s align on it.” That changes how product teams negotiate scope, prioritize, and move forward. Worth your 2 hours. Test it on a real problem, not a toy prompt. What’s the one flow you’d prototype first? #ProductManagement #AI #ProductStrategy #Prototyping #EnterpriseTech #DesignSystems

I got rejected after this Interview answer 💔 Not proud of it. But this one still lives rent-free in my head. Company: One of the top product companies Round: Product Design Challenge Question: Design a feature to help users discover relevant content in our app? What I did: I jumped straight into wireframes. Added a "Recommended for You" section on the homepage, designed some cards with thumbnails and CTAs, picked nice colors, and called it a day. Result: A polite rejection email the next week. Here's what I should have actually done: Before jumping to solutions and wireframes, a good answer starts with thinking. 𝗜 𝘀𝗵𝗼𝘂𝗹𝗱'𝘃𝗲 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝗯𝘆 𝗮𝘀𝗸𝗶𝗻𝗴:  - Who are the users? (new users? power users? different personas?)  - What kind of content? (articles, videos, products, connections?)  - What does "relevant" mean? (based on past behavior? trending? personalized?)  - What's the current discovery problem we're solving?  - What are the business goals? (engagement? retention? revenue?) 𝗔 𝗳𝗹𝗼𝘄 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀: 𝟭. 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 & 𝗖𝗼𝗻𝘁𝗲𝘅𝘁  - Understand user pain points through data/interviews  - Map the current user journey  - Identify where discovery fails today 𝟮. 𝗗𝗲𝗳𝗶𝗻𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗠𝗲𝘁𝗿𝗶𝗰𝘀  - What does success look like? (time spent? click-through rate? return visits?)  - How do we measure relevance? 𝟯. 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀  - Consider multiple approaches (algorithmic, social, editorial, hybrid)  - Weigh trade-offs of each  - Don't marry one solution too early 𝟰. 𝗗𝗲𝘀𝗶𝗴𝗻 𝘁𝗵𝗲 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲  - Information architecture first, visuals later  - Think about empty states, loading states, error states  - Consider personalization vs. serendipity  - Design for accessibility and inclusion 𝟱. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 & 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝗼𝗻  - How would we test this? (A/B test? prototype testing?)  - What could go wrong?  - How do we handle edge cases? 𝟲. 𝗛𝗮𝗻𝗱𝗼𝗳𝗳 & 𝗜𝗺𝗽𝗮𝗰𝘁  - How does this scale across platforms?  - What's the technical feasibility?  - What's the rollout plan? This way, the solution is user-centered, strategic, and actually solves a real problem.

So many product teams work on new features they believe will be a game-changer for users. But how do you really know if a feature will be adopted by users? This is where UX research comes in. As UX researchers, we can help identify the probability of feature adoption by digging deep into user needs, behaviors, and expectations. Here are some ways we measure and predict feature adoption: 1. User Interviews and Surveys: By speaking directly to users, we can gauge their interest in a new feature. Through surveys or interviews, we explore how they might use the feature, what problems it would solve for them, and how it fits into their current workflows. These qualitative insights give us an early understanding of potential adoption barriers. 2. Usability Testing: A feature may seem like a great idea on paper, but how do users actually interact with it? Conducting usability tests on prototypes allows us to see whether users understand the feature, how intuitive it is, and where they might get stuck. If the feature feels cumbersome, adoption rates will likely be lower. 3. Task Success Rate: This metric allows us to measure how easily users can complete tasks using the new feature. A low success rate indicates friction, and users are less likely to adopt a feature if it doesn’t make their experience easier. 4. User Journey Mapping: By mapping out the user journey, we can see where the new feature fits into the overall user experience. Does it make sense within the flow of their tasks? Are there unnecessary steps or points of confusion? A smooth, integrated feature is more likely to be adopted. 5. A/B Testing: Once a feature is live, we can run A/B tests to see if it’s driving the desired behavior. Does the feature increase engagement or task completion compared to the previous version? These quantitative insights allow us to measure real-world adoption and refine the feature based on user interactions. 6. Feature Feedback: After a feature is released, gathering feedback is key. By monitoring user comments, satisfaction scores, and support tickets, we can understand how users feel about the feature. Are they using it as intended? Are there any pain points that need addressing? As UX researchers, our role is to validate whether a feature truly meets user needs and fits within their daily tasks. We can predict adoption rates, identify potential issues early, and help product teams make informed decisions before launching a feature. How do you measure feature adoption in your research?

Most dashboards don’t fail because of bad data or bad charts. They fail because they were built too early. Someone asks for “a dashboard.” The team opens the BI tool. SQL gets written. Charts get polished. And only at the end does anyone ask the uncomfortable question: “Wait… what decision is this supposed to support?” That’s why prototyping is non-negotiable if you want dashboards that actually get used. Prototyping forces the hard thinking before the build: → What questions are we really trying to answer? → Who is this for, and what do they need to decide? → Which metrics matter, and which ones are just noise? → What level of detail is useful vs overwhelming? A simple low-fidelity prototype (even boxes and labels) does two critical things: 1 — It exposes vague thinking immediately. 2 — It lets stakeholders react to structure and logic, not colors and charts. By the time you open your BI tool, 80% of the decisions should already be made. That’s exactly why I put together this practical, no-nonsense cheatsheet for dashboard discovery and prototyping. It’s a repeatable framework to gather clean, complete dashboard requirements — without endless meetings, vague requests, or dashboards that get ignored. The guide walks you through: – Running focused user interviews – Defining business questions first – Aligning on metrics and breakdowns – Mapping data sources – Translating all of that into a clear, stakeholder-friendly layout If your dashboards aren’t getting used, don’t add more charts. Prototype better → https://lnkd.in/dxyFYdUy