An evaluation framework helps AI product managers ship reliable AI applications by systematically testing LLM outputs. The framework involves creating datasets from production traces, running LLM-as-judge evaluations to assess quality metrics like tone and correctness, comparing human labels against automated eval results, and iterating on prompts using A/B testing. Key insight: evaluations should be treated as requirements documentation, with eval datasets serving as acceptance criteria. The approach addresses the non-deterministic nature of LLMs through structured testing workflows that combine automated evaluation with human verification.
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