How to Evaluate LLMs Before Production Deployment

LLMs that perform well on benchmarks can still encounter practical issues in production environments. This is because real-world inputs are ambiguous, labels are inconsistent, and edge cases not covered by benchmarks often cause failures.

This article shares the evaluation approach applied to reduce false positives in GitHub Secret Scanning while maintaining the safety of security workflows. The core idea is to focus on the Product Decision rather than the model itself.

The evaluation criteria consist of three levels:

• Primary Outcome: Reduction in false positives and improvement in Precision
• Safety Constraint: Maintaining Recall within acceptable limits
• Operational Guardrails: Latency, cost, reliability, and production compatibility

Offline evaluation should be approached like Integration Testing, not as a one-time task. The system must be continuously verified to prevent regressions when prompts are modified or models are changed.