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  • Balance auto-evals with the last mile of human reviews: While LLM-judges or programmatic evals provide scale, human evaluations capture nuanced quality signals that auto evals might miss.
  • Curate golden datasets: Human-annotated datasets are key to defining what “good” means for your specific use case, forming the foundation for effective offline evaluation.
  • Align LLM judges: LLM judges must be aligned with human preferences continuously to ensure they are tuned to your agent-specific outcomes.