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
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