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All digests tagged AI Evals
Learn how to build better AI evaluations using eight Claude Code skills. Focus on Error Discovery and Eval Audit to move beyond writing evals before analyzing real product failures.
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This session details the Model Cascade architecture, a technique to drastically reduce LLM classification costs (by over 90%) while maintaining high accuracy (e.g., 95%+). Instead of running an expensive 'Oracle' model on every record, the cascade uses a cheap proxy model for initial predictions and only escalates to the Oracle when the proxy's confidence score is low. The optimal decision threshold ($\tau$) is found offline by simulating performance across samples.
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