Evaluate And Secure Apple Intelligence Features Before Shipping

This article discusses how to evaluate and secure generative AI features in Apple apps before release, focusing on Apple Intelligence Foundation Models. It explains that traditional deterministic testing is insufficient for model-driven features, and introduces a new evaluation approach using the Evaluations framework and Swift Testing. The author builds a reading-list assistant as an example, covering dataset creation, metric definition, tool-call trajectory checking, and security mitigations. The goal is to create release gates that ensure acceptable output quality and user safety, rather than relying on exact string matching. The article is technical, aimed at developers working with Apple's AI frameworks.