LLM wrappers in Prompt Engineering / GenAI - Model Metrics & Evaluation

Metrics & Evaluation - LLM wrappers

Which metric matters for LLM wrappers and WHY

LLM wrappers are tools that help use large language models (LLMs) better. The key metric to check is response accuracy, which means how correct or relevant the LLM's answers are when wrapped. We also look at latency (speed) and robustness (handling different inputs well). Accuracy matters most because the wrapper should keep or improve the LLM's quality. Speed matters because users want quick answers. Robustness ensures the wrapper does not break or give bad results on tricky inputs.

Confusion matrix or equivalent visualization

For LLM wrappers, we often check classification or question-answering tasks. Here is an example confusion matrix for a classification task after wrapping an LLM:

      | Predicted Positive | Predicted Negative |
      |--------------------|--------------------|
      | True Positive (TP): 80 | False Positive (FP): 10 |
      | False Negative (FN): 20 | True Negative (TN): 90 |
    

This shows how many answers the wrapped LLM got right or wrong. We use these numbers to calculate precision and recall.

Precision vs Recall tradeoff with examples

Precision means how many answers the wrapper marked as correct really are correct. Recall means how many of all correct answers the wrapper found.

For example, if the wrapper is used in a customer support chatbot, high precision is important so it does not give wrong info. But if used in medical advice, high recall is more important to catch all possible issues.

Improving precision may lower recall and vice versa. The wrapper design should balance these based on the use case.

What "good" vs "bad" metric values look like for LLM wrappers

Good values:

• Precision and recall above 85% for classification tasks
• Low latency (under 1 second response time)
• Stable results across different inputs (robustness)

Bad values:

• Precision or recall below 50%, meaning many wrong or missed answers
• High latency causing slow responses
• Unstable or inconsistent outputs on similar inputs

Common pitfalls in metrics for LLM wrappers

• Accuracy paradox: High overall accuracy but poor performance on important classes.
• Data leakage: Wrapper accidentally uses test data during tuning, inflating metrics.
• Overfitting: Wrapper tuned too much on training data, fails on new inputs.
• Ignoring latency: Focusing only on accuracy but wrapper slows down user experience.
• Not measuring robustness: Wrapper fails silently on unusual inputs.

Self-check question

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

Answer: No, it is not good. Even though accuracy is high, the very low recall means the wrapper misses most fraud cases. In fraud detection, missing fraud is very risky. The model should have high recall to catch as many frauds as possible, even if accuracy is slightly lower.

Key Result

For LLM wrappers, balancing high precision, recall, and low latency ensures accurate, fast, and reliable outputs.