Evals Are the Product: Building Benchmarks You Can Trust
Model quality is whatever your eval measures. If the benchmark is weak, the whole team optimizes the wrong thing.
Your eval is your compass
Every decision — which checkpoint to ship, whether a prompt change helped, if a cheaper model is good enough — routes through your evaluation set. A noisy or biased benchmark quietly steers months of work in the wrong direction. Treat eval design with the same rigor as model design.
Contamination is everywhere
Public benchmarks leak into pretraining corpora. A model that scores 90% may have memorized the test set rather than learned the skill. Guard against it with held-out private splits and canary strings you can grep for.
def check_contamination(train_texts, benchmark_qs):
seen = set(train_texts)
leaked = [q for q in benchmark_qs if q in seen]
return len(leaked) / len(benchmark_qs)Pin everything
Reproducibility is non-negotiable. Fix the model version, decoding temperature, random seed, and the number of runs, then report a mean and a spread. A single-run score with no variance is a rumor, not a measurement.
Prefer task-grounded metrics
Wherever you can, score against something checkable — a unit test passing, a SQL query returning the right rows, an exact-match answer. Reserve LLM-as-judge for genuinely open-ended outputs, and validate the judge against human labels before you trust it. The best benchmark is the one that correlates with the outcome your users actually feel.
Written by the AI Blog editorial team
Deep dives written by ML engineers and researchers who ship models in production. Replace this bio with your own — a line about your background and the systems you build goes a long way with technical readers.