Evaluation and Benchmarking

A fine-tuned model feels better — but is it really? Without systematic evaluation, you're flying blind. This guide shows how to make model quality measurable and comparable.

Metrics by Use Case

Text Generation

Classification

• Accuracy, Precision, Recall, F1 Score
• Confusion Matrix for error analysis
• AUC-ROC for threshold optimization

Domain-Specific

• Medicine: Accuracy on medical benchmarks (MedQA, PubMedQA)
• Legal: Precision in contract analysis
• Code: Pass@K on HumanEval, functionality tests

The Evaluation Setup

1. Prepare Test Set

• Never use training data for testing (data leakage!)
• 80/10/10 split: Training / Validation / Test
• Test set should reflect real-world distribution
• Include edge cases and adversarial examples

2. Define Baseline

Always compare against:

• The base model (without fine-tuning)
• The best prompt engineering approach
• RAG-based solution if applicable
• Previous fine-tuning (for updates)

3. A/B Testing

                   ┌─ Model A (Baseline) ──────────────┐
Traffic (50/50) ──┤                                     ├─ Compare
                   └─ Model B (Fine-Tuned) ────────────┘

Metrics: Accuracy, latency, user satisfaction, costs
Duration: At least 1 week with statistically significant traffic

4. Regression Tests

Fine-tuning on task A can reduce performance on task B (catastrophic forgetting):

• Before training: Create benchmark on tasks A, B, C
• After training: Run all benchmarks again
• Threshold: Maximum 5% degradation on other tasks
• Fix: Diverse training data, multi-task training, regularization

Experiment Tracking

Every training run must be documented:

Tools: Weights & Biases, MLflow, Neptune

Common Evaluation Mistakes

• ❌ Only looking at loss curve (says little about real quality)
• ❌ No baseline comparison (everything "feels" good)
• ❌ Test set too small or not representative
• ❌ No regression tests after updates

Practical tip: Create an "Evaluation Playbook" with 50 test cases you run after every training. Automate what's possible — but human evaluation remains essential for style and tone.