Prompt engineering works for prototypes but consistently breaks in production. The mismatch arises because production systems handle unpredictable inputs, long-lived workflows, and changing models. Prompts themselves are difficult to version, test, or reason about as a system, leading to prompt sprawl across services and configs.

Prompts aren't true software abstractions like functions or APIs. They mix task intent, execution logic, and implicit assumptions into raw text. This makes them fragile and unsuitable as a primary interface. When they fail, teams resort to rewriting rather than debugging, which is trial and error, not engineering.

Production AI needs task-level primitives—stable operations like classifying input or extracting data. The solution is wrappers: reusable, versioned code components that encapsulate AI logic. Prompts become implementation details, not the surface interface. This approach brings stability, testability, and clear ownership, bridging the gap between powerful models and reliable systems.