How to evaluate LLMs before production

The GitHub Blog reports that benchmarks and curated datasets aren't enough to predict how a language model will perform in production. In a new post, GitHub shares lessons from building an LLM system to reduce false positives in its secret scanning feature, which flags credentials committed to repositories. The team emphasizes starting with the product decision rather than the model, treating offline evaluation like integration testing, and keeping evaluation close to production conditions. They also stress that precision and recall aren't interchangeable — for security workflows, recall is a safety constraint that must stay within a defined range.

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