When prototyping new AI-powered ideas, build them as command-line interface (CLI) tools instead of web apps. The constrained UI of the terminal forces you to focus on the core workflow and logic, preventing distraction from visual design and enabling faster shipping of a functional version.
Related Insights
Prototyping directly in the production environment makes high-quality interactions achievable without extensive resources. This dissolves the traditional design dilemma of sacrificing quality for speed, allowing teams to build better products faster.
AI coding agents enable "vibe coding," where non-engineers like designers can build functional prototypes without deep technical expertise. This accelerates iteration by allowing designers to translate ideas directly into interactive surfaces for testing.
Instead of creating multiple static mockups, prompt the AI to build a widget directly into a prototype that allows clicking through different design styles. This provides a live, interactive way to evaluate options within the actual user interface.
Non-technical founders using AI tools must unlearn traditional project planning. The key is rapid iteration: building a first version you know you will discard. This mindset leverages the AI's speed, making it emotionally easier to pivot and refine ideas without the sunk cost fallacy of wasting developer time.
Historically, resource-intensive prototyping (requiring designers and tools like Figma) was reserved for major features. AI tools reduce prototype creation time to minutes, allowing PMs to de-risk even minor features with user testing and solution discovery, improving the entire product's success rate.
Instead of providing a vague functional description, feed prototyping AIs a detailed JSON data model first. This separates data from UI generation, forcing the AI to build a more realistic and higher-quality experience around concrete data, avoiding ambiguity and poor assumptions.
A practical AI workflow for product teams is to screenshot their current application and prompt an AI to clone it with modifications. This allows for rapid visualization of new features and UI changes, creating an efficient feedback loop for product development.
Building a true AI product starts by defining its core capabilities in an AI playground to understand what's possible. This exploration informs the AI architecture and user interface, a reverse process from traditional software where UI design often comes first.
The panel suggests a best practice for AI prototyping tools: focus on pinpointed interactions or small, specific user flows. Once a prototype grows to encompass the entire product, it's more efficient to move directly into the codebase, as you're past the point of exploration.
For complex, one-time tasks like a code migration, don't just ask AI to write a script. Instead, have it build a disposable tool—a "jig" or "command center”—that visualizes the process and guides you through each step. This provides more control and understanding than a black-box script.
