How to Architect AI Products That Improve Over Time

AI products differ from traditional software in that they must be designed for continuous improvement rather than static deployment, as their behavior shifts with dynamic data, model updates, and user inputs. The article outlines four architectural principles: building feedback mechanisms into the system, separating AI logic from infrastructure through modular layers, treating data as a living asset that requires regular updates, and designing strong evaluation pipelines to measure improvement.