Testing

LLM Evaluation Frameworks in Production: Comparing Promptfoo, DeepEval, Ragas, and Inspect Architecture, Metric Calibration, and Quality Gate Economics

Testing large language model applications in production requires shifting from deterministic software unit tests to probabilistic evaluation harnesses. Traditional software engineering relies on binary assertions (assert output == expected), but generative models exhibit non-deterministic outputs, variable token distributions, and nuanced semantic drift across prompt revisions, model updates, and temperature configurations. To prevent regressions and quantify system capabilities before deployme