Designing with AI

My honest take with AI is it has raised the bar for what good design thinking actually looks like. With new tools dropping what seems like on a daily basis, the skill that matters most right now is not knowing the difference on when to prototype with Figma Make versus Replit, or when to ideate with Google Stitch versus V0. It's knowing how to think through and properly set the stage for these tools to work in the greater context of the problems. This is what I am learning across my design practice, inside of corporate product work and in the personal projects I do on my own time.

The designer becomes the integration layer

The conversation about AI in design tends to default to replacement anxiety. But, I don't think that this is where we should be posting the question. The interesting question is what it means to design when the tools themselves are rapidly becoming smarter and more savvy to understanding context.

The designer shifts from maker to curator, becoming the integration layer across systems, behavior, and context. This designer is someone who understands enough about user behavior, enough about how AI systems work, and enough about what the code can actually do to connect the parts.

This reality is a different profession than most of us were hired into. It requires fluency that is not just visual or strategic. It requires genuine curiosity about the underlying systems, a willingness to experiment, and comfort with not having a clean answer every time I sit down to work. The designers who will thrive are not the ones who found the perfect stack but rather the ones who stay in motion as things shift and keep building regardless.

Being an AI-native designer means being willing to form a POV earlier

I don't wait for a PRD or a project brief to start forming a point of view. I use AI to synthesize early signals from research, surface patterns across user insights, and pressure-test flow logic before a single frame is open.

By the time I am in Figma I have already ideated and prototyped concepts to continue refining. What that changes most is the quality of my discernment. When I can generate multiple directions quickly, my energy goes into deciding which one is actually worth pursuing rather than scrambling to produce options. That judgment, knowing what good looks like and why, how it pushes the bounds beyond what we know, is where the real value of a senior designer lives right now.

The making is faster too. I prototype concepts early and test them before the conversation moves into execution. And I stay genuinely curious about what is possible at the edges, playing with interaction patterns and moments of delight in ways that feed back into better design decisions upstream.

The beauty of your output is ultimately a byproduct of the systems connected to it and the skills you're using within your design system.

Prompting is a design presentation skill

How I prompt an AI system for a prototype under strict context is not that different from how I write a product brief or present design work. Both require clarity of intent, to anticipate edge cases, to explain the product to someone who has never interacted with it before. It requires me to know when the output is good enough and when I need to go again.

Prompting isn't a magic trick. It's a communication skill, and it's the same one that makes me a better designer: articulating a problem, providing the right context, and knowing when to push back on what I get back.

We are moving from AI as a tool query to a system with real skills it can execute within a workflow. Knowing how to direct that, scope it, and verify the output is going to be one of the defining competencies for designers over the next few years. It maps directly onto what good product thinking has always been: knowing what problem I am actually trying to solve, building something that addresses it, and being honest about what is working and what is not.

Prompting forces me to articulate the problem more precisely, and that act of precision is where the real design thinking happens. When the system is not producing the right output, the question I ask myself is what context is missing. That gap is almost always where the sharper design decision lives.

The most valuable thing AI does in my design process is compress the time between a fuzzy problem and a sharp hypothesis. The judgment about which hypothesis is worth pursuing stays with me.

Building to think more clearly

Some of the most important learning I'm doing is not happening inside a product team. It's happening in the hours I carve out to build things for myself, personal projects with no real brief besides the problems I'm having in life and am searching a solution for. That freedom is where I am developing the most transferable fluency.

My personal stack is Claude Code with the Figma MCP, Lovable, Replit, and Vercel. Lovable gets something running fast so I can test a product idea at the pace I think. Replit is where I prototype and experiment without friction. Once a concept holds up, I rebuild it in Claude Code for more precise control over the translation between design intent and actual implementation, and I deploy with Vercel. Shipping something, even a small personal project, changes how you think about design in ways that staying inside a design tool simply can't.

When I actually build things, even imperfect ones, I develop a feel for feasibility that wireframes alone cannot give me. I ask different questions in design reviews. I understand where the complexity actually lives. It's giving me a real appreciation for what it means to push code, navigate security considerations, and think about scalability as a design constraint worth understanding from the start.

What I am still figuring out

The tools I use today may not be the ones that matter in six months. What I am more confident about is the posture. Stay curious. Build things. Play with the new tools. Most importantly, don't wait for permission to understand the systems shaping your industry.