How do I test and evaluate AI features?

To test and evaluate AI features, Apple recommends moving beyond traditional unit tests and using the Evaluations framework to measure how well your intelligent feature behaves across many inputs.

Key ideas

• Collect a dataset that covers:
  • your main use cases
  • edge cases
  • safety/unhappy paths
• Run the feature end to end with an automated evaluation pipeline.
• For small datasets, inspect responses manually.
• For larger datasets, use a model judge to automatically grade results.
• Use:
  • quantitative metrics when you can measure something in code
  • qualitative metrics when the judgment is more subjective, via a model judge
• Iterate: update prompts, instructions, or feature logic, then rerun the evaluation to see whether scores improve.

From the sessions

• Meet the Evaluations framework explains how to build evaluations, run them, and use model judges and score dimensions to assess quality.
• Explore prompt design & safety for on-device foundation models recommends curating datasets for both quality and safety, testing unhappy paths, and scaling evaluation with another language model to grade responses automatically.
• Create robust evaluations for agentic apps adds techniques for synthetic data, tool-calling evaluations, and comparing results over time.

Practical approach

1. Define what “good” means for your feature.
2. Build a representative test dataset.
3. Automate evaluation runs.
4. Use metrics or judges to score outputs.
5. Review failures and refine.
6. Repeat until the scores are where you want them.

If you want, I can also summarize the best practice checklist Apple gives for evaluation and testing.