Automated evaluation metrics in Prompt Engineering / GenAI - Model Metrics & Evaluation

Metrics & Evaluation - Automated evaluation metrics

Which metric matters for Automated evaluation metrics and WHY

Automated evaluation metrics help us quickly check how well a model is doing without needing humans every time. The right metric depends on the task:

• Accuracy measures overall correct predictions, good for balanced classes.
• Precision tells us how many predicted positives are actually correct, important when false alarms are costly.
• Recall shows how many real positives we found, key when missing positives is bad.
• F1 Score balances precision and recall, useful when both matter.
• AUC (Area Under Curve) measures how well the model separates classes, useful for ranking tasks.

Choosing the right metric helps us understand if the model fits our real-world needs.

Confusion matrix example

      |                    | Predicted Positive | Predicted Negative |
      |--------------------|--------------------|--------------------|
      | Actual Positive     | True Positive (TP)  | False Negative (FN) |
      | Actual Negative     | False Positive (FP) | True Negative (TN)  |

      Example numbers:
      TP = 70, FP = 10, TN = 900, FN = 20

      Total samples = 70 + 10 + 900 + 20 = 1000
    

From this, we calculate:

• Precision = 70 / (70 + 10) = 0.875
• Recall = 70 / (70 + 20) = 0.778
• Accuracy = (70 + 900) / 1000 = 0.97
• F1 Score = 2 * (0.875 * 0.778) / (0.875 + 0.778) ≈ 0.823

Precision vs Recall tradeoff with examples

Precision and recall often pull in opposite directions:

• High Precision, Low Recall: The model is careful and only predicts positive when very sure. Good for spam filters so normal emails aren't marked spam.
• High Recall, Low Precision: The model catches almost all positives but may include many false alarms. Good for cancer detection so no cancer case is missed.

Automated metrics help us find the right balance based on what matters more in our problem.

What "good" vs "bad" metric values look like

For automated evaluation metrics, here is what to expect:

• Good: High precision and recall (above 0.8) means the model predicts well and finds most positives.
• Bad: Low precision (<0.5) means many false alarms; low recall (<0.5) means many missed positives.
• Accuracy: High accuracy (>0.9) is good if classes are balanced, but can be misleading if data is skewed.
• F1 Score: A balanced score above 0.7 is usually acceptable.

Common pitfalls in automated evaluation metrics

• Accuracy paradox: High accuracy can hide poor performance if classes are imbalanced.
• Data leakage: When test data leaks into training, metrics look unrealistically good.
• Overfitting indicators: Very high training metrics but low test metrics mean the model memorizes instead of learning.
• Ignoring context: Using the wrong metric for the problem can mislead decisions.

Self-check question

Your model has 98% accuracy but only 12% recall on fraud cases. Is it good for production? Why or why not?

Answer: No, it is not good. The model misses 88% of fraud cases (low recall), which is dangerous. High accuracy is misleading because fraud is rare, so the model mostly predicts non-fraud correctly but fails to catch fraud.

Key Result

Automated evaluation metrics like precision, recall, and F1 score provide clear, task-relevant insights to judge model quality beyond simple accuracy.