Your model scored 92% on public benchmarks, but it’s failing on 30% of your real-world production tickets.

This episode breaks down the benchmark saturation paradox: why static academic benchmarks fail to predict production accuracy, how clean training data creates false confidence, and how to build a continuous evaluation pipeline from real production trace failures.

Keywords: LLM evals, benchmark saturation, model evaluation, MMLU, production AI, continuous evals, AI infrastructure, prompt engineering, edge cases

This is Maya. New episodes three times a week.

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