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GitHub draws a set of practices for evaluating LLM systems before production from an evaluation experiment involving a language-model-based system designed to reduce false positives in secret scanning. The experiment emphasizes linking metrics to the product decision, simulating the actual operating environment, and analyzing errors, while treating offline evaluation as structured evidence rather than a guarantee of behavior in every production scenario.