AI‑generated code creates implicit architectural decisions, forcing teams to rely on experimentation to validate quality attributes. To get useful results from AI, teams must clearly express trade‑offs and reasoning so the model can generate solutions aligned with desired QARs.

To architect effectively for an MVP, teams must answer three questions in order: Is the business idea worth pursuing? What performance and scalability are needed? How much maintainability and supportability are required? These guide Minimum Viable Architecture decisions. Empirical testing helps reject costly assumptions early and adapt architecture as the MVP evolves.