● TOPIC
Model Evaluation
How to evaluate AI models without being blinded by benchmarks: evals, SWE-Bench, signal vs noise, testing with real product data, and the pitfalls of public rankings.
model evaluationevalsAI benchmarkSWE-BenchLLM evaluationmodel assessment
[ FREQUENTLY ASKED QUESTIONS ]
FAQ
Q.01 Can I trust public AI model benchmarks?
With caution. OpenAI found about 30% of SWE-Bench Pro tasks were broken or poorly defined. Benchmarks can be contaminated by training data, have poorly specified tasks, or not reflect your real use case. Use benchmarks as a directional signal, not as a final decision.
Q.02 How to evaluate a model for my product?
Build an evaluation dataset with real cases from your product. Measure accuracy on your specific task, not on a generic benchmark. Test for regressions when updating models. Monitor quality in production with sampling. And consider total cost (tokens, latency, infrastructure), not just maximum capability.
[ RELATED CONCEPTS ]
VIEW ALL →