A repeatable framework for comparing LLM outputs in production is essential as prompts, models, and routing logic evolve. Manual review and ad-hoc prompting are insufficient at scale due to inconsistency and lack of baselines. Effective evaluation requires tracking metrics across three dimensions: hallucination detection,
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Is manual review and one-off prompting enough to compare LLM outputs?Metrics to consider when comparing LLM outputsHow Arize approaches evalsCompleting the loop with Portkey: routing and orchestration for evaluationsSort: