As of 2025-09-30, using JSON-based prototyping has proven to be a tactical advantage for AI product managers aiming to create predictable and testable AI prototypes. By shifting from free-form prompts to a structured JSON approach during product discovery, PMs can ensure that data and UI elements are cleanly separated, which leads to more accurate prototyping outcomes. Here’s a step-by-step approach for evaluating JSON-based prototyping:
1. Define a Structured Data Schema: Begin by using tools like Claude and integrate reliable data sources. Outline your prototype by drafting a JSON schema that includes detailed product data, such as itineraries, user personas, or feature outlines. 2. Generate Reliable Mock Data: Leverage existing APIs to populate your JSON with real-world data. For example, integrating real photo URLs via Unsplash MCP servers ensures that the mock data is accurate and reduces issues like broken link references. 3. Iterate with Clear Prompts: Instead of vague prompts, use consistent and structured JSON outputs (e.g., including fields like itinerary name, travelers, daily items, etc.) to guide the AI’s output process. This helps in maintaining consistency and predictability. 4. Validate and Test: Run the generated outputs through your UI development process and conduct user tests to further refine the prototype. Validate that each component of your UI accurately reflects the structured data.
Adopting this method not only standardizes the prototyping process but also makes future iterations and bug fixes more manageable. As of 2025-09-30, early implementation reports suggest that shifting to JSON improves prototype predictability, with detailed case studies from early adopters beginning to emerge.