When Can You Actually Trust a Machine Learning Model?

<p><strong>FIRST</strong></p> <p><strong>Why Not Just Use LLM-as-Judge?</strong></p> <p>Many teams default to using another LLM to evaluate their agent. It's easy. No labels

<p>Your fraud detection model hits 99.8% accuracy. Ship it?</p> <p>Not so fast. That number means your model predicts "not fraud" for every single transaction — and it's right

<p>Most companies shipping AI have never once tried to break it.</p> <p>Not because they don't care about security. Because they assume the model handles it. The model was trained

<p>A while back, I had to retract one of my own results.</p> <p>A model I'd built was posting a validation score I was genuinely proud of. Then I looked closer: the test set

<p>Large language models are incredibly powerful, but they are not automatically deterministic.</p> <p>Ask the same question twice and you may get slightly different answers. Ask