Why Accuracy Is Not Enough: Evaluation Metrics Every AI Engineer Should Understand

Neetika Mittal Posted on May 30 • Originally published at mneetika.github.io Why Accuracy Is Not Enough: Evaluation Metrics Every AI Engineer Should Understand # machinelearning # ai # softwareengineering # llm Why Accuracy Is Not Enough: Evaluation Metrics Every AI Engineer Should Understand Your evaluation dashboard says your model is 95% accurate . Leadership is happy. The deployment goes live. Two weeks later, users complain that critical failures are still slipping through. The problem is not always the model. Sometimes the problem is the metric. As AI systems move from research prototypes into production infrastructure, evaluation becomes one of the most important engineering problems. This is especially true for modern GenAI systems, where outputs are probabilistic, subjective, and highly context dependent. In this article, we will break down the most important evaluation metrics used in machine learning and GenAI systems, understand where they fail, and discuss how to think about evaluation from a production engineering perspective. The Core Problem With Accuracy Accuracy is usually the first metric people encounter in machine learning. It is simple: A c c u r a c y = C o r r e c t P r e d i c t i o n s T o t a l P r e d i c t i o n s Accuracy = \frac{Correct\ Predictions}{Total\ Predictions} A cc u r a cy = T o t a l P re d i c t i o n s C orrec t P re d i c t i o n s ​ At first glance, it seems reasonable. If a model predicts correctly 95% of the time, surely that sounds good. But accuracy becomes dangerous when datasets are imbalanced. Imagine a fraud detection system: 99% of transactions are legitimate 1% are fraudulent Now suppose your model predicts: "Every transaction is legitimate." The result? 99% accuracy Completely useless fraud detection To make the failure more obvious, imagine 10,000 transactions: Metric Count Fraudulent transactions 100 Legitimate transactions 9,900 Fraud cases detected 0 Fraud cases missed 100 The model gets 9,900 predic