Many users blame AI tools for generic designs when the real issue is a poorly defined initial prompt. Using a preparatory GPT to outline user goals, needs, and flows ensures a strong starting point, preventing the costly and circular revisions that stem from a vague beginning.

AI prototyping doesn't replace the PRD; it transforms its purpose. Instead of being a static document, the PRD's rich context and user stories become the ideal 'master prompt' to feed into an AI tool, ensuring the initial design is grounded in strategic requirements.

Instead of facing a blank canvas, create a custom GPT that asks a series of structured questions (e.g., product goal, target user, key flows). This process extracts the necessary context to generate a focused, high-quality initial prompt for prototyping tools.

Don't ask an AI agent to build an entire product at once. Structure your plan as a series of features. For each step, have the AI build the feature, then immediately write a test for it. The AI should only proceed to the next feature once the current one passes its test.

The most effective application of AI isn't a visible chatbot feature. It's an invisible layer that intelligently removes friction from existing user workflows. Instead of creating new work for users (like prompt engineering), AI should simplify experiences, like automatically surfacing a 'pay bill' link without the user ever consciously 'using AI.'

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.

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.

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.