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How to evaluate LLMs before production
GitHub improved their secret-scanning accuracy by 95% by ditching generic benchmarks in favor of evaluating models against production-like ambiguity.
What: Principal Applied Scientist Mariko Wakabayashi details how GitHub versions prompts, models, and input configurations like code to test LLMs against real-world production conditions.
Why it matters: This demonstrates a shift away from static LLM benchmarks toward rigorous, data-driven evaluation pipelines that treat model outputs as software components requiring integration testing.
Deep dive
- Stop relying on generic LLM benchmarks for production readiness.
- Version control your prompts, model configurations, and evaluation datasets together.
- Establish evaluation pipelines that mimic production-level noise and ambiguity.
- Measure success based on business-critical metrics like false-positive reduction rather than leaderboard scores.
- Implement guardrails that can be independently validated through offline testing.
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