GitHub emphasizes product-focused LLM evaluation

GitHub’s engineering team highlighted lessons from evaluating an LLM for secret scanning, where the goal was to reduce noisy alerts while preserving security. Strong performance on curated datasets did not guarantee reliability against ambiguous, noisy production data, making standard benchmarks insufficient for production decisions.

The evaluation centered on a product question: whether the system could reduce false positives while maintaining sufficient recall to remain safe. Precision was treated as the primary outcome, while recall served as a safety constraint because missing a real credential would be more damaging than asking a developer to review an unnecessary alert. Latency, cost, and reliability were considered operational guardrails.

GitHub treats offline evaluation as end-to-end integration testing, rerunning it after significant changes to prompts, models, datasets, or system configuration. Each run is recorded to support reliable comparisons, and experiments isolate major variables before combining changes. The approach offers a practical framework for moving LLM systems from promising prototypes to dependable production tools.