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.

In an age of rapid AI prototyping, it's easy to jump to solutions without deeply understanding the problem. The act of writing a spec forces product managers to clarify their thinking and structure context. Writing is how PMs "refactor their thoughts" and avoid overfitting to a partially-baked solution.

Moving PRDs and other product artifacts from Confluence or Notion directly into the codebase's repository gives AI coding assistants persistent, local context. This adjacency means the AI doesn't need external tool access (like an MCP) to understand the 'why' behind the code, leading to better suggestions and iterations.

Product Requirement Documents (PRDs) are often written and then ignored. AI-generated prototypes change this dynamic by serving as powerful internal communication tools. Putting an interactive model in front of engineering and design teams sparks better, more tangible conversations and ideas than a flat document ever could.

To get consistent, high-quality results from AI coding assistants, define reusable instructions in dedicated files (e.g., `prd.md`) within your repository. This "agent briefing" file can be referenced in prompts, ensuring all generated assets adhere to a predefined structure and style.

Unlike tools that immediately generate code from a prompt, Replit first engages in a planning phase. It collaborates with the user to define the structure and goals before execution. This structured, plan-first approach makes it a far stronger and more useful tool for product managers.

Instead of just asking an AI to write a PRD, first provide it with a "Socratic questioning" template. The LLM will then act as a thinking partner, asking challenging, open-ended questions about the problem and solution. This upfront thinking process results in a significantly more robust final document.

A powerful but unintuitive AI development pattern is to give a model a vague goal and let it attempt a full implementation. This "throwaway" draft, with its mistakes and unexpected choices, provides crucial insights for writing a much more accurate plan for the final version.

Instead of writing detailed Product Requirement Documents (PRDs), use a brief prompt with an AI tool like Vercel's v0. The generated prototype immediately reveals gaps and unstated assumptions in your thinking, allowing you to refine requirements based on the AI's 'misinterpretations' before creating a clearer final spec.