Your AI isn't too weak. Your evals are missing.

The article delves into the importance of proper evaluation methods for AI models, using the example of Trackist, an app that generates personalized workout plans based on user inputs. The author reveals that while the app's AI seems to work fine based on superficial checks, it lacks rigorous evaluation metrics to truly measure its effectiveness. This oversight highlights a broader issue in AI development, where qualitative assessments often fail to capture the model's true performance, suggesting that more robust evaluation techniques are essential for reliable AI applications.