Evaluation metrics

We want to know if AI is doing a good job. Not just “working,” but working in ways we can trust. That’s where evaluation metrics come in. Think of them as report cards—simple scores that tell us if she’s paying attention or just guessing.

Accuracy

Accuracy is the broad score. Out of all the answers she gave, how many were right? It’s quick and satisfying, like a grade on a multiple-choice test. But it hides detail. A model can look “accurate” while still missing the things we care about most.

Precision

Precision asks a sharper question: when she says “yes,” how often is she right? High precision means fewer false alarms. Good for situations where a wrong “yes” is worse than silence—like spam filters or medical alerts.

Recall

Recall is the flip side. Out of all the real “yes” cases, how many did she catch? High recall means fewer misses. We want this when missing something is costly—finding tumors, catching fraud, spotting security holes.

F1 score

F1 score splits the difference. It balances precision and recall into one number. Useful when both matter, and we don’t want to game the system by maximizing one while tanking the other. It’s like forcing fairness into the grade.

BLEU

BLEU (bilingual evaluation understudy) handles text generation. It checks how close her translations or summaries are to human examples. Not perfect—matching words isn’t the same as matching meaning—but it’s still a handy ruler for messy language tasks.

We should keep metrics in their place: signals, not gospel. They help us steer, but they don’t drive. Sometimes it feels like we spend more time grading than learning, but maybe that’s the price of building something we can trust.