Source
How to evaluate LLMs before production
GitHub outlines a framework for evaluating LLMs before production, based on their work reducing false positives in secret scanning. The method prioritizes realistic offline testing over curated benchmarks to ensure production readiness.
Why it matters
This approach ensures AI systems are reliable and safe before deployment. It reduces the time developers spend on false security alerts while ensuring real threats are not missed.
The details
- GitHub achieved a 95% reduction in false positives on the evaluated offline dataset.
- Evaluation criteria are categorized into primary outcomes, safety constraints, and operational guardrails.
- Offline evaluations are treated as repeatable integration tests for every meaningful change.
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