An Engineer's Guide to AI Code Model Evals

AI code model evaluations (evals) are crucial for assessing and enhancing coding-capable AI models. These structured tests define correctness criteria for model outputs, focusing on functional accuracy by running generated code against expected results or unit tests. The "hill climbing" approach uses evals to guide iterative improvements, analyzing failures to enhance model capabilities. Goldens serve as benchmarks for comparing outputs and guiding human evaluation. By designing effective eval tasks and aligning them with developer needs, AI teams can systematically improve coding models.