You can't improve what you don't measure. Proper model evaluation is the difference between a system that works and one that doesn't.
The Evaluation Pyramid
Bottom Layer - Automated Metrics: BLEU, ROUGE, BERTScore measure surface-level similarity. Fast but incomplete.
Middle Layer - Human Evaluation: Score outputs on relevance, correctness, clarity. Time-intensive but essential for quality assurance.
Top Layer - Production Metrics: User satisfaction, business impact, cost per prediction. The real measure of success.
Key Metrics for LLMs
- Latency: How fast is the response? Aim for <100ms for interactive use
- Accuracy: How often is it correct? Depends on task (factuality, summarization, etc.)
- Hallucination Rate: How often does it make things up? Measure with fact-checking
- Cost per Request: Token usage × price. Track by user and use case
Benchmarking Best Practices
Create a test set that represents real usage. Don't use training data or marketing examples.
Establish baselines for comparison. "Our model improved by 15%" only matters relative to previous performance.
Test edge cases and failures. A model that works 95% of the time but fails catastrophically 5% of the time is dangerous.
Avoiding Evaluation Pitfalls
- ❌ Only using automated metrics
- ❌ Testing on data you trained on
- ❌ Ignoring failure cases
- ❌ Not tracking baseline performance
Remember: Good evaluation catches problems early. Bad evaluation catches them when users discover them.