I've been successfully integrating the Foundation Models framework into my healthcare app using structured generation with @Generable schemas. While my initial testing (20-30 iterations) shows promising results, I need to validate consistency and reliability at scale before production deployment.
Question
Is there a recommended approach for automated, large-scale testing of Foundation Models responses?
Specifically, I'm looking to:
Automate 1000+ test iterations with consistent prompts and structured schemas
Measure response consistency across identical inputs
Validate structured output reliability (proper schema adherence, no generation failures)
Collect performance metrics (TTFT, TPS) for optimization
Specific Questions
Framework Limitations: Are there any undocumented rate limits or thermal throttling considerations for rapid session creation/destruction?
Performance Tools: Can Xcode's Foundation Models Instrument be used programmatically, or only through Instruments UI?
Automation Integration: Any recommendations for integrating with testing frameworks?
Session Reuse: Is it better to reuse a single LanguageModelSession or create fresh sessions for each test iteration?
Use Case Context
My wellness app provides medically safe activity recommendations based on user health profiles. The Foundation Models framework processes health context and generates structured recommendations for exercises, nutrition, and lifestyle activities. Given the safety implications of providing health-related guidance, I need rigorous validation to ensure the model consistently produces appropriate, well-formed recommendations across diverse user scenarios and health conditions.
Has anyone in the community built similar large-scale testing infrastructure for Foundation Models? Any insights on best practices or potential pitfalls would be greatly appreciated.
Topic:
Machine Learning & AI
SubTopic:
Foundation Models