Benchmark

Benchmarks give the AI field a shared ruler: they’re standardized tests — sometimes academic, sometimes industry-defined — that let you compare models in a consistent way. MMLU (Massive Multitask Language Understanding) tests knowledge across subjects; HumanEval tests code generation; TruthfulQA tests resistance to common misconceptions. While benchmarks are useful for broad comparisons, they have real limitations — models can be inadvertently trained to perform well on specific benchmarks without improving on real-world tasks, a form of Goodhart’s Law. For behavior architects, public benchmarks are a starting point, but purpose-built internal evaluations tailored to your actual use case will almost always be more meaningful than third-party scores.