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

1. Benchmark evaluation: Standardized tests (MMLU, HumanEval, MATH) that enable model comparison
2. Task-specific evaluation: Custom tests for your particular use case
3. Human evaluation: Expert or crowd-sourced ratings of quality, helpfulness, safety
4. Automated evaluation: Using another LLM to judge outputs ("LLM-as-judge")
5. 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:

1. Unit tests: Specific input-output pairs that must be correct
2. Benchmark suites: Standardized tests for broad capability assessment
3. Domain evaluation: Task-specific tests with domain expert review
4. Integration tests: End-to-end system performance including retrieval, tools, etc.
5. Production monitoring: Continuous evaluation on real user interactions