LLM Evaluation Frameworks and CI/CD Quality Gates in Production: Comparing DeepEval, Ragas, Promptfoo, and TruLens
Moving large language model applications from exploratory prototypes to production systems requires automated quality validation. Relying on manual inspection or unstructured testing introduces regression risk across model updates, prompt edits, and retrieval modifications. Automated evaluation frameworks address this by converting probabilistic model outputs into measurable, repeatable software assertions. While traditional unit testing relies on deterministic assertions, production LLM testin
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