If you're still prototyping the same way you were pre-AI, two years ago, I've got bad news: you're not prototyping, you're storyboarding.

Don’t get me wrong—this is still an integral part of product design. It's fast, collaborative, and it makes stakeholders feel like they're seeing the product. But the static prototypes you build in Figma Design aren’t always the best reflection of how your product actually behaves in the wild. 

Static prototypes have failed product designers

Most product designs suffer from "happy path syndrome," showing the perfect scenario: user clicks A, goes to B, and completes C. But if you’ve been in Product long enough, you know that real products don't work like that.

What happens when data loads slowly? What if a filter returns zero results? What if someone uses your advanced analytics feature in a way your Figma frames never anticipated?

Traditional prototyping tools excel at showcasing visual design and basic user flows, but they fall short when it comes to demonstrating complex interactions, state management, and data processing. This gap between prototype and reality usually leads to pricey revisions during development.

AI has entered the chat

Generative AI has made dynamic prototyping ridiculously more accessible. Tools like Claude, Cursor, Loveable, and Figma Make can quickly take your design intent and turn it into working front-end code.

Even if you're not an engineer (or have those skills), genAI tools can help bridge the gap between your ideas and an interactive test. 

These tools understand design patterns, can generate responsive layouts, and even handle complex logic flows that would take hours to mock up in traditional prototyping tools.

When we design new analytics features, we skip Figma Design entirely and prototype in code with real datasets because you can't evaluate value with static frames alone. 

Our product team uses AI to create prototypes that behave like the final product, complete with realistic data processing and edge case handling.

What you can do with code-based prototyping

Here's what product teams are getting when they move to code-first prototypes with AI:

  • Test the real thing, not a simulation. When your prototype processes actual data, you see how the product behaves, not just how it looks. You can observe performance bottlenecks, understand how complex datasets affect user experience, and identify usability issues that only emerge with real content.
  • Faster feedback loops. You don't need to wait for a dev sprint to test logic. You can build, share, and refine—all in the same week. This acceleration means you can iterate on core functionality while the idea is still fresh and stakeholder interest is high.
  • Explore edge cases, not just the sunny-day flow. You can simulate unusual states, filters, and user inputs to see what breaks. This early detection of edge cases prevents costly surprises during development and QA phases.
  • Better alignment across product, design, and engineering. When designers, product managers, and engineers are all looking at the same interactive prototype, communication improves.

How to begin with AI-powered prototyping

Ready to level up your prototyping game? Here's your roadmap:

  1. Pick a tool. Claude, Lovable, or Figma Make are great places to start. Each has strengths: Claude Artifacts excels at quick and simple prototypes, Lovable for more sophisticated applications, and Figma Make if you already have Figma designs that you want to turn into rich interactive experiences.
  2. Build a tiny slice. Take one high-impact flow and turn it into a live prototype. Don't try to prototype your entire product. Instead, focus on the core interaction that needs validation. Maybe it's a dashboard filter system or a complex form with conditional logic.
  3. Keep Figma Design. Use it for layouts, visual design, and handoff polish. The goal isn't to replace your existing tools but to complement them with dynamic prototyping where it matters most.
  4. Optional: Pair up with a dev. Get some support if the AI stumbles. Having an engineer available for consultation can help you navigate technical constraints and ensure your prototype translates well to production code.

Designing software experiences in the age of AI

The landscape of product development is shifting rapidly. Teams that can prototype and validate complex interactions quickly will have a significant advantage over those still relying solely on static mockups.

Code-based prototypes help you answer questions that static design frames simply can't: How does this feature perform with 10,000 data points? What happens when users combine filters in unexpected ways? How does the experience degrade on slower connections?

The future of product development belongs to teams that can rapidly iterate on functional prototypes, validate assumptions with real data, and bridge the gap between design and development.