LLM evaluation guide: production readiness

The article addresses a common pitfall: relying on clean benchmarks to predict production performance. GitHub's secret scanning work illustrates that real inputs are ambiguous, and offline metrics may not translate to production. They prioritize precision (reducing false positives) while keeping recall within a safety bound, and they use operational guardrails like latency and cost to decide if a change is deployable.

Their approach treats evaluation as an ongoing process similar to integration testing. They rerun evaluations on any meaningful change, record every configuration, and change one variable at a time to attribute improvements correctly. They also stress that offline evaluation must mirror the production task, including context and formatting, to avoid false confidence.

They also caution against treating production labels as absolute truth, since workflow outcomes may not indicate true positives. To fill gaps, they use synthetic data for rare cases but note it should supplement, not replace, production-like data. The article suggests a systematic, product-first mindset for LLM deployment.