A Scaffold at the Gate: Building AI Applications That Can Evolve

The rapid generation of code using AI assistants introduces the risk of creating applications that are difficult to maintain, understand, and extend. To mitigate this, data scientists should adopt established software engineering principles like SOLID and Clean/Hexagonal Architecture. This approach involves using a baseline or "scaffold" to create a reusable starting structure for AI applications. This scaffold separates core application logic from external dependencies, ensuring the system is testable, flexible, and can evolve without requiring a complete redesign.