Why fine-tuning adapts models to domains in Prompt Engineering / GenAI - Why Metrics Matter

Metrics & Evaluation - Why fine-tuning adapts models to domains

Which metric matters for this concept and WHY

When fine-tuning a model to a specific domain, the key metrics to watch are accuracy, precision, and recall. These show how well the model adapts to the new domain data. Fine-tuning aims to improve these metrics on domain-specific tasks compared to the original model.

Confusion matrix or equivalent visualization

    Confusion Matrix Example (Domain-Specific Task):

          Predicted
          Pos   Neg
    Actual Pos  85    15
           Neg  10    90

    Total samples = 200
    TP=85, FP=10, TN=90, FN=15
    

Precision vs Recall tradeoff with concrete examples

Fine-tuning helps balance precision and recall for the domain. For example, in medical diagnosis, recall is critical to catch all cases, so fine-tuning focuses on reducing missed positives. In spam detection, precision is key to avoid marking good emails as spam, so fine-tuning improves precision.

What "good" vs "bad" metric values look like for this use case

Good: After fine-tuning, precision and recall both improve significantly on domain data, e.g., precision > 0.85 and recall > 0.80.

Bad: Metrics stay low or only one improves, e.g., precision 0.50 and recall 0.30, showing poor adaptation.

Metrics pitfalls

• Overfitting: Fine-tuning too much can make the model only work well on training domain data but fail on new examples.
• Data leakage: Using test data during fine-tuning inflates metrics falsely.
• Ignoring recall or precision: Focusing on one metric can hurt overall usefulness.

Self-check question

Your fine-tuned model has 98% accuracy but only 12% recall on domain positives. Is it good for production? Why or why not?

Key Result

Fine-tuning improves domain-specific precision and recall, key to adapting models effectively.