Prototype Evaluation Practices

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.

Two types of PMs are emerging from the AI prototyping wave. The first group learned to build. They can spin up a working prototype in 45 minutes. They demo it the next day. Stakeholders approve it because working software is more convincing than a PowerPoint. Then metrics don’t move. Nobody tested “red shoes size 10 wide” and watched the AI parse “wide” as a style descriptor. Nobody counted the clicks and realized AI search adds 2 steps over the existing filter sidebar. Nobody asked engineering about API costs at production traffic. $40K/month, unbudgeted. They went from writing bad specs to building bad prototypes. Same failure mode, just faster. The second group learned to evaluate. Boris Cherny’s Claude Code team prototyped the terminal spinner 50-100 times. 80% didn’t ship. Agent teams went through hundreds of versions. The condensed file view took 30 prototypes then a month of internal dogfooding. Boris ships 20-30 PRs a day. But the 80% he kills are more important than the 20% he ships. “Half my ideas are bad. I don’t know which half until I try.” The skill that separates these two groups is what I’m calling taste at speed: the ability to evaluate working software fast, kill most of it, and ship the survivors. A PM who reviews one spec per month builds judgment from 12 data points per year. A PM evaluating 15 prototypes per week builds judgment from 780. Same role. Same year. 65x more pattern-matching reps. That gap compounds every single week. I wrote the complete guide: 1. Why taste at speed is the defining PM skill (with the printing press analogy that changed how I think about this) 2. How Boris’s team actually works (5 parallel terminals, plan mode, phone-first agents) 3. The 5 Lenses evaluation framework (problem-solution fit, interaction cost, edge cases, technical debt, business model) 4. How to build this skill at any level (never prototyped, can prototype, ready to change your team) 5. Where the PRD fits now (it moved from step 2 to step 6) 6. A full real-world teardown showing the same feature evaluated by two PMs with wildly different outcomes Plus 4 downloadable templates: a Prototype Evaluation Scorecard, a Skill-Building Roadmap, a Prototype-First PRD Template, and a Divergent Prototyping Prompt Template. Full guide for subscribers: https://lnkd.in/g-HmamRS Not everyone can be Boris. Most PMs have meetings from 9 to 5 and a company that still requires PRDs. But a director who prototypes one feature per month makes dramatically better decisions because of it. A parent doing one prototype per sprint is already ahead of 90%. The reps compound regardless of volume.

Most AI apps don’t fail at “building.” They fail at “proving.” If your demo looks great but your outputs aren’t reliable, your app won’t stand out—especially in a challenge. Your AI can be brilliant… and still confidently wrong. That’s why evaluation is the missing layer between prototype and production. (The deck calls this out clearly: without evaluation you get unpredictable behavior + silent failures.) The “Report Card” that makes your AI app stand out When you ship (or submit) an AI app, test it like an exam—not like a vibe check. 1) Build your “exam” dataset Create 10–20 gold-standard examples (real questions + ideal answers). Include edge cases from real user behavior (confusing, incomplete, adversarial prompts). Generate variations to expand coverage. 2) Grade with a simple rubric Use a rubric like: Correctness (factually accurate?) Relevance (answers the question?) Hallucination (made-up content?) Contextual Relevancy (RAG) (did retrieval actually help?) Responsible AI (bias/toxicity?) 3) Combine machines + humans Automated checks = fast, repeatable, scalable Human review = gold standard for nuance Best principle: let people set the standard; let machines enforce it at scale. Why this matters for the Building AI Application Challenge In a room full of similar apps, evaluation is your differentiator: You don’t just claim “my bot is good” You show a report card, failure cases, improvements, and reliability metrics If you’re in the Building AI Application Challenge, don’t stop at “it works.” Add an Evaluation Report Card to your submission—this is how your app stands out to judges, recruiters, and real users.

Google Cloud's New AI Agent Guide: From Prototype to Production in 3 Steps Ever wondered why most AI agent projects never make it past the demo stage? Google Cloud just released a comprehensive technical guide that tackles this exact problem. After analyzing hundreds of startup failures, they identified the three critical gaps between building a cool prototype and running a reliable AI agent in production. 👉 Why most AI agents fail The core issue isn't the AI itself - it's treating agents like traditional software. Unlike deterministic code, AI agents make decisions you can't predict. They might work perfectly in testing, then fail spectacularly with real users. Most teams focus on the fun part (building the agent) while ignoring the hard parts (evaluation, monitoring, and safety). This creates a dangerous gap between "it works on my machine" and "it works for customers." 👉 What Google's approach offers The guide introduces three key frameworks: Agent Development Kit (ADK) - A code-first toolkit for building multi-agent systems that can actually talk to each other through open protocols AgentOps methodology - Systematic evaluation that goes beyond "vibe testing" to measure reasoning quality, factual accuracy, and safety Agent Starter Pack - Production infrastructure templates that handle deployment, monitoring, and continuous evaluation automatically 👉 How to implement this Start with the evaluation framework. Before writing any agent code, define how you'll measure success across four layers: component testing, reasoning evaluation, output quality, and live monitoring. Use the ReAct pattern (Reason-Act-Observe) as your cognitive architecture. This creates traceable decision paths you can debug and improve. Deploy with built-in observability from day one. The Agent Starter Pack configures monitoring, logging, and evaluation pipelines automatically. The guide includes real customer stories from companies like Box and BioCortex, showing how this systematic approach accelerated their development while maintaining reliability. Worth reading if you're building anything beyond simple chatbots.

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.

How to apply AI systems to become AI-fluent (Part 3) Part 1 was about prototyping ideas. Part 2 was about strategic analysis. This one is about something more overlooked, but arguably more valuable: building evaluation frameworks. Most people use AI to generate outputs. Very few know how to systematically judge whether those outputs are actually good. The real leverage is not in generation. It is in the evaluation. 1. Start with a domain relevant to your work. Sales emails. Marketing copy. Product requirement docs. Investment memos. Code snippets. Policy proposals. Anything where quality matters. 2. Prompt: “Generate five strong examples of X.” Treat this as raw material. Next prompt: “Create a scoring rubric to evaluate these outputs. Include 5–7 criteria with clear definitions and a 1–5 scale.” Do not accept vague criteria like “good” or “clear.” Push for specificity. What does a 5 actually look like? What separates a 3 from a 4? 3. Now apply the rubric. Ask AI to score each example and justify the rating. Where is the reasoning thin? Where is it subjective? Where could the rubric be gamed? Revise the framework until it feels rigorous. 4. Then stress test it. Generate new examples and rescore them. Does the rubric still hold? Would a domain expert agree with the evaluation? What criteria are missing? What you are actually developing is the ability to define standards, reduce subjectivity, and make quality measurable. This helps you learn to articulate what “good” looks like in a structured, defensible way. You can turn this into a real artifact. A documented scoring framework or repeatable review process that your team could adopt. On a resume, this can be added as: • Designed a structured evaluation rubric for AI-generated outputs • Built a measurable quality scoring framework to standardize review • Reduced subjectivity in content assessment through defined criteria AI fluency at this level goes beyond writing better prompts. It is about building systems that decide what excellence means. Test this out and let me know how it works for you

Prototypes aren’t for testing your product. They’re for finding the assumptions that will become engineering rework. Most teams get this backward. - They build a polished flow. - They walk stakeholders through the happy path. - Everyone agrees it “makes sense.” - Then engineering starts building. That is when the real product shows up. → What happens when the user cancels halfway through? → What state does the record enter? → What data gets saved? → What permissions apply? → What happens when the API times out? And just as important: → What edge cases are out of scope? → What behavior should the system refuse? None of that was locked. The prototype looked complete. The product behavior was not. That gap is where the Rework Tax starts. Not because the design was bad. Because the prototype created a false signal of build-readiness. System integrity is the foundation of user experience. If the logic fails, the “experience” is just a high-fidelity lie. A prototype should not be treated as a visual artifact. It should be treated as an interrogation layer. The goal is not to ask, “Does this look right?” The goal is to ask, “Is this behavior defined well enough to build?” ✺ Low fidelity should expose whether the workflow is even executable. ✺ Medium fidelity should expose missing states, decision paths, flow logic, and handoff ambiguity. ✺ High fidelity should expose whether the team is ready to commit the behavior to the system. The mistake is treating fidelity like a maturity ladder. It is not. Fidelity is a risk-control mechanism. Use the lowest fidelity required to expose the next expensive assumption. Anything more creates false confidence. Anything less hides the risk. This matters even more now that teams are feeding prototype logic into AI coding agents. In a manual build, seniority can act as a safety net. A senior engineer may pause, challenge the gap, and force the missing decision into the room. In an AI-assisted build, that safety net gets weaker. Cursor, Bolt, and other AI build tools are High-Speed Yes Men. They do not stop when the logic is incomplete. They do not ask whether a state is invalid. They do not challenge a missing edge case. They simply turn ambiguity into functioning software. That is how a prototype becomes production rework. That is how undefined behavior becomes Automated Chaos. The prototype is not the product. The prototype is the last cheap place to find out whether your team actually understands what engineering is about to build.

I have been developing Agentic Systems for more than two years now and the same patterns keep emerging. 👇 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗗𝗿𝗶𝘃𝗲𝗻 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 is the only way how you can be successful in building your 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 - here is my template. Let’s zoom in: 𝟭. Define a problem you want to solve: is GenAI even needed? 𝟮. Build a Prototype: figure out if the solution is feasible. 𝟯. Define Performance Metrics: you must have output metrics defined for how you will measure success of your application. 𝟰. Define Evals: split the above into smaller input metrics that can move the key metrics forward. Decompose them into tasks that could be automated and move the given input metrics. Define Evals for each. Store the Evals in your Observability Platform. ℹ️ Steps 𝟭. - 𝟰. are where AI Product Managers can help, but can also be handled by AI Engineers. 𝟱. Build a PoC: it can be simple (excel sheet) or more complex (user facing UI). Regardless of what it is, expose it to the users for feedback as soon as possible. 𝟲. Instrument your application: gather traces and human feedback and store it in an Observability Platform next to previously stored Evals. 𝟳. Run Evals on traced data: traces contain inputs and outputs of your application, run evals on top of them. 𝟴. Analyse Failing Evals and negative user feedback: this data is gold as it specifically pinpoints where the Agentic System needs improvement. 𝟵. Use data from the previous step to improve your application - prompt engineer, improve AI system topology, finetune models etc. Make sure that the changes move Evals into the right direction. 𝟭𝟬. Build and expose the improved application to the users. 𝟭𝟭. Monitor the application in production: this comes out of the box - you have implemented evaluations and traces for development purposes, they can be reused for monitoring. Configure specific alerting thresholds and enjoy the peace of mind. Learn all of this hands-on in my End-to-End AI Engineering Bootcamp starting in 2 weeks (10% off this week): https://lnkd.in/djvtszk5 ✅ 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: ➡️ Run steps 𝟲. - 𝟭𝟬. to continuously improve and evolve your application. ➡️ As you build up in complexity, new requirements can be added to the same application, this includes running steps 𝟭. - 𝟱. and attaching the new logic as routes to your Agentic System. ➡️ You start off with a simple Chatbot and add a route that can classify user intent to take action (e.g. add items to a shopping cart). What is your experience in evolving Agentic Systems? Let me know in the comments 👇

User testing is a great way to get early feedback from your users. But many teams don't put much thinking into it...they jump straight to a clickable prototype (typically in Figma) and put it in front of users. Rather the teams who crush this take it up a notch! They begin with asking: - What assumptions are we testing? - Who will we test with? - How do these assumptions show up in the prototype? - What will the prototype test? What will it NOT? - How do we intend to perform the user testing? (e.g. is it virtual or in person?) From there we can begin to determine what's the best type of prototype to use. I use the below matrix with teams to help them decide and decipher the different kinds of prototypes: Depending on what you're trying to test you might want to go with high or low fidelity and also depending on your skills/access to specialist capabilities you might choose to go with a technical or low tech approach. HIGH-FIDELITY / LOW TECH e.g. Interactive Mock-Ups Testing: Usability HIGH-FIDELITY / HIGH TECH e.g. Pilot, Beta, AB/404 Tests Testing: Desirability LOW-FIDELITY / LOW TECH e.g. Wire Frames, Mock-Ups Testing: Viability LOW-FIDELITY / HIGH TECH e.g. Proof-of-Concepts Testing: Feasibility Hope that helps! Bonus, here's a template for planning your prototypes: https://lnkd.in/gV25w7WN #ProductManagement #DesignThinking #ProductDesign #ProductDiscovery

✒️ “Evaluating #Socialinnovation Prototypes: A Guide” is a practical Social Innovation Canada guidebook intended for social innovators to help them "make more effective use of #prototypes" and by doing so iterate fast and learn more. 💡 Authors provided multiple frameworks expanding on the traditional criteria for #learning and #testing prototypes, by including dimensions that take into account the #complexity of social challenges such as : 🎯 Effectiveness : How likely is it that the promising solution will generate the intended results (or negative ones)? How might it be adapted to maximize the former possibility and minimize the latter? 🎯 Ethical : Does the promising solution support (or undermine) human rights or ethical commitments? How might it be adapted to provide stronger support? 🎯 Sustainability : How will the promising solution positively or negatively contribute to bio-diversity, limits on pollution and/or GHG emissions? How might it be adapted to generate more positive contributions? 🎯 Scalability : Can the innovation be scaled for greater impact? Or will it succeed only in one location and/or at a smaller scale? 🎯 Supportability (Broader than desirability) : Will stakeholders translate their desire for a promising solution into concrete support for it in the near future? How might it be adapted to facilitate that transition?