How to evaluate LLMs before production

When GitHub decided to enhance their secret scanning capabilities with large language models, they faced a critical question: How do you know if an LLM is actually ready for production? This isn’t just an academic concern—deploying an LLM that misses secrets or generates false positives can have real security implications. GitHub’s engineering team shared their evaluation framework, and it offers practical guidance for anyone considering LLMs in their own systems. The core insight is straightforward but often overlooked: benchmark testing against your specific use case matters far more than raw model size or hype.

Secret scanning is a perfect case study for why careful evaluation is essential. The task seems simple—detect API keys, credentials, and tokens in code—but the reality is nuanced. A model might excel at identifying common patterns like AWS keys while completely missing proprietary token formats your company uses. GitHub’s approach involved creating comprehensive test datasets that reflected real-world scenarios they encounter, then systematically measuring each model’s precision (false positive rate) and recall (missed secrets rate). They ran inference tests on various models, tracked latency and cost per request, and evaluated how different LLMs handled edge cases. The technical work involved setting up evaluation pipelines that could run thousands of test cases efficiently—exactly the kind of automation infrastructure any organization needs before trusting an LLM in production.

The practical takeaway applies broadly across AI deployments: your evaluation methodology should mirror your production constraints. If latency matters, test latency under realistic load. If cost is a concern, calculate true cost-per-prediction including API calls and failures. If reliability is critical—and in security scanning it absolutely is—establish acceptable thresholds for both precision and recall before you start. GitHub found that their ideal model wasn’t necessarily the largest or most capable general-purpose LLM, but rather the one that best balanced their specific needs around accuracy, speed, and cost. This required building evaluation infrastructure: test data, metrics collection, and reproducible testing environments.

For teams integrating LLMs into cloud applications or automation workflows, the lesson is clear: invest in evaluation before production deployment. Create realistic test sets that reflect your actual data, not just benchmark datasets. Measure the metrics that matter for your use case—whether that’s accuracy, latency, cost, or all three. Use version control for your prompts and evaluation results so you can track how model behavior changes over time. Most importantly, treat LLM evaluation as part of your standard deployment pipeline, the same way you’d handle load testing or security scanning for any other infrastructure change. The upfront work saves you from costly production incidents and keeps your systems reliable.