{"id":13473,"date":"2026-03-17T19:15:13","date_gmt":"2026-03-17T19:15:13","guid":{"rendered":"https:\/\/cheesecakelabs.com\/blog\/"},"modified":"2026-03-17T19:15:15","modified_gmt":"2026-03-17T19:15:15","slug":"exploring-ai-prototyping-tools","status":"publish","type":"post","link":"https:\/\/cheesecakelabs.com\/blog\/exploring-ai-prototyping-tools\/","title":{"rendered":"What We Learned Exploring AI Prototyping Tools"},"content":{"rendered":"\n

For years, product design prototypes <\/strong>essentially meant the same thing: clickable flows, simulated states, and a lot of imagination to explain what didn\u2019t yet exist. Over the past few months, however, AI-powered prototyping tools have begun to change that dynamic \u2014 especially for complex digital products with complex data structures, business rules, and multiple user flows.<\/p>\n\n\n\n

In this article, I share a hands-on exploration conducted from a product design perspective, testing AI-based prototyping tools<\/a> currently available in the market. The goal wasn\u2019t to determine which tool is \u201cbest\u201d, but to understand when each one makes sense, what UX risks it introduces, and how it can (or cannot) accelerate the design process.<\/p>\n\n\n\n

This content is grounded in applied research \u2014 real attempts, missteps, iterations, and lessons learned \u2014 not a polished demo.<\/p>\n\n\n\n

The starting point: Accelerating execution, not strategy<\/h2>\n\n\n\n

Before discussing tools, it\u2019s important to clarify a principle that guided the entire exploration:<\/p>\n\n\n\n

AI accelerates execution. It does not define strategy.<\/strong><\/p>\n\n\n\n

None of the tools tested can replace product decisions, contextual understanding, or UX judgment. When that distinction isn\u2019t made explicit from the start, the risk is significant: over-reliance on AI, unrealistic expectations of speed, or acceptance of \u201cfunctional\u201d solutions that ultimately degrade the user experience.<\/p>\n\n\n\n

Another early insight was recognizing that AI requires context <\/strong>\u2014 and a fallback plan. Across all experiments, one thing became clear: there is no single AI solution for the entire product process. The value lies in placing each tool at the right moment within the workflow.<\/p>\n\n\n\n

Read more:<\/strong> From Testable Code to AI-Driven Testing: How Frontend Developers Can Finally Trust Their Tests<\/a><\/p>\n\n\n\n

AI behaves differently at each stage of the process<\/h2>\n\n\n\n

One of the main findings was that the same tool can be highly effective in one phase and risky in another. Below are the key takeaways organized by project stage.<\/p>\n\n\n\n

1. Discovery and early definition: Explore without over-control<\/strong><\/h3>\n\n\n\n

At this stage, precision is not the goal. The objective is to generate perspective, spark discussions, and unlock conversations with stakeholders.<\/p>\n\n\n

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\"Tools<\/figure>\n<\/div>\n\n\n

Recommended use:<\/strong> visual brainstorming, fast prototypes for early discussions, and alignment.<\/p>\n\n\n\n

2. End of definition: Making it tangible without losing control<\/strong><\/h3>\n\n\n\n

As the project takes shape \u2014 with clearer flows, defined scope, and higher fidelity \u2014 the expected behavior from AI changes significantly.<\/p>\n\n\n\n

At this stage, tools like Cursor and Claude Code, when integrated with Figma via MCP, showed meaningful advantages:<\/p>\n\n\n\n