HOW TO EVALUATE LLMS BEFORE PRODUCTION

Summary from the TLDR InfoSec newsletter

When evaluating an LLM-based system to reduce false positives in GitHub secrets scanning, the team faced issues that made benchmarks unrepresentative of production workloads and prevented them from properly evaluating model performance. To combat this problem, the team at GitHub created an LLM evaluation framework. First, teams should define the product decision that the system should support; then teams should make sure that offline experiments use a representative dataset and have only one clearly defined variable; error analysis and LLM-as-judge systems can be used to guide human analysts towards interesting aspects of the evaluation; and then finally the experiment can be promoted to production.