LLM Evaluation2 articles

LLM Evaluation

Articles

LLM Evaluation and Automated Testing Frameworks in Production: Comparing Ragas, DeepEval, Promptfoo, and TruLens Architecture, Synthetic Testsets, LLM-as-a-Judge Economics, and CI/CD Pipelines
Evaluating large language model applications in production requires transitioning from subjective manual spot-checking to automated, reproducible testing pipelines. Unlike traditional software unit tests where deterministic inputs produce exact expected outputs, LLM-driven applications exhibit non-deterministic generation, complex multi-hop retrieval dynamics, and open-ended tool interactions. Deploying updates to prompts, retrieval parameters, embedding models, or base LLM checkpoints introduc
August 28, 20261 min
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
August 26, 20261 min