Evaluating AI models isn't like testing traditional software. There's no simple "pass/fail" — outputs are probabilistic, subjective, and context-dependent.
The Evaluation Challenge
Traditional software testing checks deterministic behavior: given input X, expect output Y. But LLMs:
- Produce different outputs for the same input (temperature > 0)
- Can be "correct" in multiple ways ("The capital of France is Paris" vs. "Paris is the French capital")
- Exhibit emergent behaviors not captured by simple metrics
- Perform differently across domains, languages, and task types
Types of Evaluation
- Benchmark evaluation: Standardized tests (MMLU, HumanEval, MATH) that enable model comparison
- Task-specific evaluation: Custom tests for your particular use case
- Human evaluation: Expert or crowd-sourced ratings of quality, helpfulness, safety
- Automated evaluation: Using another LLM to judge outputs ("LLM-as-judge")
- Red teaming: Adversarial testing to find failures, biases, and safety issues
Common Pitfalls
- Benchmark contamination: Training data that includes benchmark questions inflates scores
- Goodhart's Law: When a metric becomes a target, it ceases to be a good metric
- Cherry-picking: Showing the best outputs while hiding failures
- Single-metric fixation: Optimizing for one metric (e.g., accuracy) at the expense of others (safety, latency)
- Static evaluation: Testing once and assuming performance remains constant
The Evaluation Pyramid
Build evaluation from the bottom up:
- Unit tests: Specific input-output pairs that must be correct
- Benchmark suites: Standardized tests for broad capability assessment
- Domain evaluation: Task-specific tests with domain expert review
- Integration tests: End-to-end system performance including retrieval, tools, etc.
- Production monitoring: Continuous evaluation on real user interactions