GitHub Shares Lessons on Evaluating LLMs for Production

GitHub’s latest blog explains how teams can move an LLM from prototype to production without losing the gains seen on clean benchmarks. Using a secret‑scanning system that flags potential credentials, the authors show that real‑world inputs are often ambiguous, mislabeled, or missing context, so offline metrics can mislead. They recommend starting with a clear product decision—what mistakes are acceptable—and then defining guardrails: a precision‑driven primary goal, a recall safety constraint, and operational limits on latency, cost, and reliability. Evaluation is treated like integration testing: every prompt, model, or logic change is run in isolation, versioned, and compared to a baseline so that improvements or regressions can be traced to a single variable.