How to evaluate LLMs before production (github.blog)

Microsoft's Principal Applied Scientist Mariko shared insights on evaluating large language models (LLMs) as they transition to production, highlighting significant challenges that arise beyond initial benchmarking. While benchmarks are useful for early model testing, they often fail to address the complexities of real-world data, where ambiguity, inconsistent labeling, and edge cases can lead to critical errors. For instance, her team's work on an LLM system for GitHub secret scanning focused on balancing the reduction of false positives with the need to maintain adequate recall, emphasizing that not all metrics are interchangeable and precision can be prioritized over recall, given the higher stakes in security contexts. Mariko outlined a structured evaluation approach that includes defining product decisions ahead of model adjustments, treating offline evaluations similarly to integration tests, and ensuring that offline evaluations closely resemble production tasks. She stressed the importance of tracking changes methodically and considering production labels as signals rather than absolute truths, as they can reflect various workflow outcomes that complicate the evaluation of model performance. Ultimately, this comprehensive evaluation framework not only enhances the robustness of LLM systems across various applications but also emphasizes the need for ongoing assessments as models evolve and the environments in which they operate change.