● 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

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1 EDITIONS

[ FREQUENTLY ASKED QUESTIONS ]

FAQ

Q.01Can 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.02How 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.

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