How to evaluate LLMs before production

Effective evaluation of production LLM systems requires more than strong benchmark results, especially in security-sensitive applications such as GitHub secret scanning, where reducing false positives must not compromise recall. The authors recommend beginning with a clearly defined product decision, treating precision or false-positive reduction as the primary outcome while setting recall, latency, cost, reliability, and compatibility as safety and operational guardrails. Offline evaluation should closely reproduce production inputs, context, formatting, and pipeline behavior; be repeatable through versioned prompts, models, datasets, and configurations; and isolate major variables so improvements and regressions can be attributed accurately. Production labels should be examined critically because workflow outcomes may not represent true ground truth, while synthetic, open, and manually reviewed data can address coverage gaps and difficult edge cases. Aggregate metrics should be supplemented by error analysis that identifies whether failures arise from the model, prompt, context, pipeline, dataset, or labels, and LLM-as-judge methods can help triage cases for human review without replacing human judgment. Using these practices, the team reported a 95% reduction in false positives on its offline dataset while maintaining its recall guardrail, providing evidence to support controlled online experimentation rather than guaranteeing production performance.

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