LLM Evaluation in Production: GitHub Shares Best Practices

Evaluating large language models (LLMs) for production use is a complex challenge that requires more than just benchmark scores. GitHub's Mariko Wakabayashi and Zixiao Chen recently shared their insights on using LLMs to improve secret scanning, a feature that identifies sensitive credentials in code repositories.

The team emphasized the need to test LLM performance with production-like data, accounting for edge cases, inconsistent inputs, and real-world constraints such as latency and cost. They outlined a structured framework for evaluation, prioritizing precision, recall, and operational feasibility.

Wakabayashi and Chen defined three core evaluation criteria: primary outcome (metrics like false-positive reduction and precision), safety constraint (ensuring recall stays within acceptable limits to avoid security gaps), and operational guardrails (practical considerations such as latency, cost, reliability, and production compatibility).