Engineering Design Process Steps

One of the most important relationships at any tech company: engineering and design. When this partnership falters, brilliant ideas die on the vine. When it thrives, just about anything is possible. Since I joined in 2015, we've tested many ways to partner across disciplines. The traditional "designers create, then throw specs over the wall to engineers" approach? That’s long gone. Here's what works for us: 1. Erase the handoff mentality entirely Our strongest teams have designers and engineers working in parallel from day one. Engineers join design discussions early, providing technical guidance while concepts are still fluid. This prevents the scenario of a beautiful design proves technically impossible after weeks of work. 2. Create rapid feedback loops Julie Wang is an engineer on our team who has partnered really well with design. A tip she shared recently: "I send screen recordings at all milestones so designers can critique early." The earlier this partnership starts, the more time engineers have to fix bugs, too. 3. Value hybrid skills Our most successful products come from teams where engineers understand visual principles and designers grasp technical constraints. When team members can translate between these worlds, implementation remains true to the vision. 4. Communicate constantly – not just at milestones We've use dedicated Slack channels where work-in-progress is shared continuously. Questions are answered in minutes, not days. 5. V1s, not MVPs We've officially banned the term "MVP" at Duolingo – a policy that received spontaneous applause when I mentioned it at #Config2025 recently. Instead, we focus on shipping "V1s" that genuinely meet our quality standards. Your first version should be something you're proud of, not something you're apologizing for. Big picture: if the relationship between engineering and design is strong and fluid – and everyone has a sense of ownership – there is no ceiling to what you can build. 

I'd like to discuss using Customer Feedback for more focused product iteration. One of the most direct ways to understand customers needs and desires is through feedback. Leveraging tools like surveys, user testing, and even social media can offer invaluable insights. But don't underestimate the power of simple direct communication – be it through emails, chats, or interviews. However, while gathering feedback is essential, ensuring its quality is even more crucial. Start by setting clear feedback objectives and favor open-ended questions that allow for comprehensive answers. It's also pivotal to ensure a diversity in your feedback sources to avoid any inherent biases. But here's a caveat – not all feedback will be relevant to every customer. That's why it's essential to segment the feedback, identify common themes, and use statistical methods to validate its wider applicability. Once you've sorted and prioritised the feedback, the next step is actioning it. This involves cross-functional collaboration, translating feedback into product requirements, and setting milestones for implementation. Lastly, once changes are implemented, the cycle doesn't end. Use methods like A/B testing to gauge the direct impact of the changes. And always, always return to your customers for follow-up feedback to ensure you're on the right track. In the bustling world of tech startups, startups that listen, iterate, and refine based on customer feedback truly thrive. #startups #entrepreneurship #customer #pmf #product

Want to know the #1 most important lesson I’ve learned about Designer/Engineer handoff? 👉 Make friends with your Engineers! It’s a two way street. Make it more of a conversation with them about what is feasible and if there are any constraints. I have noticed that if they feel heard and part of the process, everyone will be happier! What does this look like in practice? Early on in your design process, make sure you are reaching out to your Engineers to learn about the timing and difficulty of what you are thinking. Learn from them about what takes time and why certain designs may be more difficult. Each project or even team could have different answers. This will depend on the code itself or even the Engineers skill levels. This is important as it will allow for less surprises or unmet deadlines at the end. From the other side, it creates a relationship with your Engineers that I have noticed tends to lead to more collaboration. This open communication encourages questions about the designs which ultimately I’ve noticed leads to more pixel perfect designs!

Most PMs are taking the wrong approach to becoming more technical. You see the pattern: - They struggle in engineering discussions - Feel imposter syndrome kicking in - Then decide "I need to learn to code" This is exactly backward. Here's why: When PMs try to match engineers' technical knowledge, they're solving the wrong problem. Engineers don't need you to understand every technical detail. They need you to: ↳ Provide clarity on what to build ↳ Help them understand customer pain points ↳ Make informed trade-off decisions The real secret to communicating with engineers isn't about becoming an engineer. It's about mastering the art of asking the right questions. A few months ago, Irene Yu (Founder of SkipLevel and former Amazon engineer) shared a powerful framework in our Supra Podcast, doing exactly this. She calls it the FAIR framework: F - Feasibility ↳ What's technically possible? ↳ What are our system constraints? ↳ What dependencies should we consider? A - Alternative Solutions ↳ What other approaches could work? ↳ What are the trade-offs? ↳ How else might we solve this? I - Impact (Short & Long-term) ↳ How will this affect system performance? ↳ What maintenance challenges might arise? ↳ How will this scale as we grow? R - Risk Management ↳ What are potential failure points? ↳ How can we mitigate these risks? ↳ What guardrails should we put in place? Let me be clear: There's nothing wrong with learning to code if you're genuinely interested in building things on the side. That's awesome. But don't sign up for coding classes as a remedy for a challenging relationship with engineering. That's trying to solve the wrong problem with the wrong solution. Next time you feel that urge to sign up for coding classes... Your engineering team doesn't need another dev. They need a product leader who can ask the right questions. What other frameworks do you use to collaborate with engineering teams?

How do you figure out what truly matters to users when you’ve got a long list of features, benefits, or design options - but only a limited sample size and even less time? A lot of UX researchers use Best-Worst Scaling (or MaxDiff) to tackle this. It’s a great method: simple for participants, easy to analyze, and far better than traditional rating scales. But when the research question goes beyond basic prioritization - like understanding user segments, handling optional features, factoring in pricing, or capturing uncertainty - MaxDiff starts to show its limits. That’s when more advanced methods come in, and they’re often more accessible than people think. For example, Anchored MaxDiff adds a must-have vs. nice-to-have dimension that turns relative rankings into more actionable insights. Adaptive Choice-Based Conjoint goes further by learning what matters most to each respondent and adapting the questions accordingly - ideal when you're juggling 10+ attributes. Menu-Based Conjoint works especially well for products with flexible options or bundles, like SaaS platforms or modular hardware, helping you see what users are likely to select together. If you suspect different mental models among your users, Latent Class Models can uncover hidden segments by clustering users based on their underlying choice patterns. TURF analysis is a lifesaver when you need to pick a few features that will have the widest reach across your audience, often used in roadmap planning. And if you're trying to account for how confident or honest people are in their responses, Bayesian Truth Serum adds a layer of statistical correction that can help de-bias sensitive data. Want to tie preferences to price? Gabor-Granger techniques and price-anchored conjoint models give you insight into willingness-to-pay without running a full pricing study. These methods all work well with small-to-medium sample sizes, especially when paired with Hierarchical Bayes or latent class estimation, making them a perfect fit for fast-paced UX environments where stakes are high and clarity matters.

Most mechanical designers are terrible at the most important skill for concept development… Sketching. Here are my top 5 recommendations for developing and communicating concept designs with less effort and more clarity. 1. Stay out of CAD for as long as possible. The tools we use to design have a profound impact on the final form of any design. CAD is no exception to this. Just look at the difference between a 1960s Jaguar F-type and 1990 Volkswagen Rabbit. No matter how fast you are at CAD, you can be faster with a pen. This is worth investing in, and the only way to do so is to resist the urge to jump into CAD and start extruding. 2. Start with pencil & paper. This is like practicing a chest pass in basketball. There is no faster, freer, cleaner, or more fun way to communicate a mechanical concept than to use a pencil and paper. A 30 second sketch can obviate the need for 1/2 a day of CAD. Concept design is all about driving the right conversations, and asking the right questions early. 3. Try out Procreate. Grab your iPad and purchase Procreate. It’s the single best app for creating beautiful sketches with digital editing and coloring capability. I use them for all our engineering guide illustrations like this. 4. Sketch over CAD screenshots. Need to work through a modification to an existing design? Want to verify if a concept will work at the proper scale? Try printing out a view of CAD or writing directly on top of a drawing. 5. Study the fundamentals. You studied heat transfer, and fluids, and statics,…why not study drawing? Pick up a book like Scott Robertson’s How to Draw and give it 10 minutes of practice every morning. In 6 months you’ll be better than 90% of your coworkers.

Stop treating design case studies like documentation. Start treating them like movies. The best design case studies I've reviewed follow a visual-first narrative: - Start with the money shot: Show the final product in context, hero images that make an impact - Set the scene: Visual problem statement showing the before state - Build tension: Key challenges visualized through early explorations - Show the journey: Process shots that highlight pivotal decisions - Reveal the payoff: Results and impact through before/after comparisons Keep text minimal. Let visuals do 80% of the storytelling. Your portfolio should feel like a gallery walk, not a reading assignment. For early-career designers: - Document everything while designing - Capture work-in-progress screenshots - Take photos of whiteboard sessions - Record user testing sessions A great case study shows the story of change - from chaos to clarity, from problem to solution. Make that transformation visible.

Design metrics guide designers to tell better stories. Perhaps paradoxically, a data-informed process makes designers better storytellers. Engaging an audience, especially in business, takes years to master, as stakeholders can be critical. However, staying focused on the value created during the process keeps stakeholders engaged and more forgiving of presentation issues. While presentation is essential, it's the use of concrete measurements to explain decisions that genuinely builds trust and credibility. Why? Using metrics in design critiques builds trust by making the process transparent and relatable. It provides measurable impact, showing how good design influences actions and economic outcomes. Additionally, it forces simplicity and clarity, allowing designers to communicate effectively with short, impactful, easily understood sentences. Here’s the surprising part. Even poor results can help create a compelling story. Using metrics allows designers to find value even when making poor decisions. Benchmarking these decisions helps everyone learn from potential problems and guides designers to better solutions. Once designers see their role as guiding the team to better outcomes rather than creating perfect solutions, storytelling can help bring everyone along in the design process. Using data in continuous research and iterative design can be complex, but it boils down to two main points in a presentation: 1. Hunch: How will a design concept improve the user experience and business results? We call this a hunch. 2. Measurement: How does a concept perform compared to other iterations? We use UX metrics as leading indicators. ↓ In our design process, we use rapid iteration to capture UX metrics using Helio. Here’s an example: → Point 1: How will a design concept improve the user experience and business results? Redesigning a university’s degree page with a guide and better search functionality can enhance user experience and increase successful applications. This hunch sets a clear focus for the presentation on expected positive outcomes. → Point 2: How does a concept perform compared to other iterations? Multiple versions of the registration page are tested for user satisfaction and task completion rates. Using Helio for rapid testing helps identify the best design, adding credibility to the presentation by showcasing data-informed decisions and measurable improvements. Combining these points into a cohesive narrative helps our design team tell a compelling story. This builds confidence in the process and demonstrates the tangible benefits and data-informed decisions that lead to a better user experience.

User research is great, but what if you do not have the time or budget for it........ In an ideal world, you would test and validate every design decision. But, that is not always the reality. Sometimes you do not have the time, access, or budget to run full research studies. So how do you bridge the gap between guessing and making informed decisions? These are some of my favorites: 1️⃣ Analyze drop-off points: Where users abandon a flow tells you a lot. Are they getting stuck on an input field? Hesitating at the payment step? Running into bugs? These patterns reveal key problem areas. 2️⃣ Identify high-friction areas: Where users spend the most time can be good or bad. If a simple action is taking too long, that might signal confusion or inefficiency in the flow. 3️⃣ Watch real user behavior: Tools like Hotjar | by Contentsquare or PostHog let you record user sessions and see how people actually interact with your product. This exposes where users struggle in real time. 4️⃣ Talk to customer support: They hear customer frustrations daily. What are the most common complaints? What issues keep coming up? This feedback is gold for improving UX. 5️⃣ Leverage account managers: They are constantly talking to customers and solving their pain points, often without looping in the product team. Ask them what they are hearing. They will gladly share everything. 6️⃣ Use survey data: A simple Google Forms, Typeform, or Tally survey can collect direct feedback on user experience and pain points. 6️⃣ Reference industry leaders: Look at existing apps or products with similar features to what you are designing. Use them as inspiration to simplify your design decisions. Many foundational patterns have already been solved, there is no need to reinvent the wheel. I have used all of these methods throughout my career, but the trick is knowing when to use each one and when to push for proper user research. This comes with time. That said, not every feature or flow needs research. Some areas of a product are so well understood that testing does not add much value. What unconventional methods have you used to gather user feedback outside of traditional testing? _______ 👋🏻 I’m Wyatt—designer turned founder, building in public & sharing what I learn. Follow for more content like this!

Here are a few management lessons I've learned from SpaceX engineers: 🙌 Empower Teams with Transparent Communication SpaceX values transparency at all levels, especially in management. Leaders are expected to communicate openly about challenges, timelines, and technical obstacles. This creates an environment where teams have clear expectations and can make informed decisions. Adopting transparent communication ensures alignment between teams and leadership, enabling more effective problem-solving. When everyone understands the priorities and challenges, it reduces bottlenecks and fosters a culture of accountability and collaboration. 🥇 Push for Aggressive Timelines Without Sacrificing Quality One of the distinguishing management practices at SpaceX is its ability to push teams toward aggressive timelines while still maintaining a focus on quality. Elon Musk famously sets ambitious (even crazy) deadlines to push the limits of what teams believe is possible, but it’s paired with an uncompromising commitment to technical excellence. Setting high expectations can drive innovation and rapid progress, but only when coupled with a clear focus on ensuring quality. Managing this balance is key to driving both speed and reliability in product development. 💡 Encourage Cross-Disciplinary Collaboration Teams work closely across different domains—avionics, propulsion, software, GNC, and more. This close collaboration ensures that all subsystems are optimized not just for individual performance but for the whole system. Promoting cross-disciplinary teamwork helps break down silos and ensures that every team understands the broader context of the product. This approach results in more integrated, cohesive systems, as well as faster identification and resolution of issues across departments. Cross-disciplinary collaboration also fosters new solutions by combining different perspectives and expertise. #venture #deeptech #spacetech #managment #engineering #product