To get superior results from AI coding agents, treat them like human developers by providing a detailed plan. Creating a Product Requirements Document (PRD) upfront leads to a more focused and accurate MVP, saving significant time on debugging and revisions later on.

An AI prototype is a powerful artifact that details user experience and functional requirements. However, it doesn't replace the Product Requirements Document (PRD). The PRD remains essential for outlining the strategic "why"—market differentiation, user acquisition, and monetization—which a prototype cannot convey.

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.

During product discovery, Amazon teams ask, "What would be our worst possible news headline?" This pre-mortem practice forces the team to identify and confront potential weak points, blind spots, and negative outcomes upfront. It's a powerful tool for looking around corners and ensuring all bases are covered before committing to build.

Before a major initiative, run a simple thought experiment: what are the best and worst possible news headlines? If the worst-case headline is indefensible from a process, intent, or PR perspective, the risk may be too high. This forces teams to confront potential negative outcomes early.

Treating AI risk management as a final step before launch leads to failure and loss of customer trust. Instead, it must be an integrated, continuous process throughout the entire AI development pipeline, from conception to deployment and iteration, to be effective.

Even for a simple personal project, starting with a Product Requirements Document (PRD) dramatically improves the output from AI code generation tools. Taking a few minutes to outline goals and features provides the necessary context for the AI to produce more accurate and relevant code, saving time on rework.

The temptation to use AI to rapidly generate, prioritize, and document features without deep customer validation poses a significant risk. This can scale the "feature factory" problem, allowing teams to build the wrong things faster than ever, making human judgment and product thinking paramount.

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.