From mockups to momentum: How vibe coding is changing the design process

Prototyping workflows with live content creates a shared space for learning

Illustration by Aaron Lowell Denton

If you’re a product designer, you know that until recently, illustrating application behavior and user experience meant sharing mockups, annotations, detailed documentation, and click-through prototypes. The ability to test design ideas with real user content, system constraints, edge cases, or probabilistic, model-driven experiences usually didn’t appear until deep in the production phase.

Teams learned to live with a certain amount of back-and-forth collaboration with engineers because static design artifacts couldn’t fully account for system constraints and conditions. The results were often costly and frustrating. Implementation became the point at which trade-offs surfaced, and edge cases were worked out. Critical learning with our creative users was pushed downstream and polish was compressed into the final stages, when changes are more difficult and costly.

AI-assisted coded prototyping, or vibe coding, changes that dynamic. By enabling designers to test their designs and workflows with live content, ideas can be explored under more realistic conditions before product requirements solidify.

Living wireframes change collaboration

Every evolution in design tools has increased fidelity and changed participation. Hand-drawn paper prototypes once invited everyone to the table. Click-through wireframes like Balsamiq made interaction more accessible but were linear and one-dimensional. Tools like Figma significantly improve collaboration, yet the prototypes are still largely linear and removed from the realities of live application logic—abstracting constraints, timing, data, and state management.

AI-assisted coding tools create a new kind of artifact between mock-up and production code. These “living wireframes” let designers express and test intent in a dynamic environment and craft working “designed experiences” where ideas respond to real input and reflect actual system behavior. This creates an opportunity for teams to engage before production begins—when learning, iterating, and change are still cheap. Engineers encounter design ideas as behaviors, not specs. Designers spend less time polishing pixels in isolation and more time helping product teams learn what works. And product managers get earlier signals before scope hardens.

These prototypes move learning forward and surface timing questions, interaction edge cases, and system assumptions when teams can still change course. The tradeoff is that the work becomes messier earlier, which can feel uncomfortable for teams accustomed to “clean” handoffs. But clean handoffs were never truly clean—problems were simply deferred—and “messier earlier” often means fewer surprises later.

One of the biggest risks in this shift is misunderstanding what these new artifacts are: A coded prototype is not a shortcut around engineering rigor. That boundary is still explicit: prototype versus production and exploration versus commitment.

AI-assisted coding and designing for probable outcomes

As AI capabilities become more embedded in products, designers are increasingly shaping how people experience generative AI features. Instead of designing fixed outcomes, they’re designing how systems respond across a range of inputs, contexts, and levels of uncertainty. These behaviors are probabilistic rather than deterministic.

In this environment, prototyping is no longer just about previewing interfaces or simulating flows. It's a way for designers to explore how a system thinks, adapts, and responds in use. Rather than specifying exact outcomes, designers use prototypes to test conditions, boundaries, and feedback loops, and learn how different choices influence system behavior over time. This shift can initially feel uncomfortable because designing for "possible" outcomes means giving up some control over precision and embracing ambiguity earlier in the process.

But this type of prototyping helps designers engage more directly with AI‑driven behavior to determine how confidence is communicated, how systems recover from ambiguity, and how users are guided when outcomes vary. It expands the role of design from defining what should happen to shaping how systems actually behave and creates space for deeper learning. Prototyping becomes a way to experience uncertainty safely and to better understand how a system behaves before behaviors are locked into production.

What changes in the end

The real value of vibe coding isn’t speed or novelty. It’s visibility.

When designers, engineers, and product stakeholders can see and interact with ideas in context, alignment stops being theoretical, and trade-offs become tangible. Conversations shift from abstract interpretation to shared experience.

When designers start prototyping behavior, the design process stops being a relay race and becomes more like a jam session. Ideas are rougher earlier. Learning happens when change is still easy. Roles overlap. Boundaries must be negotiated deliberately. And the final product benefits from having fewer late-stage surprises.

Tools will continue to evolve, and coding models will continue to improve, but a deeper paradigm shift is already here: Design is no longer just about describing what should exist; it’s about making ideas real, early enough, that teams can decide, together, whether they’re worth building at all.