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Once your evaluators are defined, you can use them in your workflow to measure performance across the dimensions that matter most for your task. Each evaluator should align with a clearly defined success criterion so results can support informed decision-making. After an evaluation completes, review overall scores and examine any identified failure patterns. Look for trends across outputs and confirm that results reflect your real-world expectations. Refine evaluator instructions if needed to ensure scoring remains consistent and meaningful, and use these insights to determine next steps. If performance meets your thresholds, move toward deployment. If not, refine training data, prompts, or model configuration, and consider synthesizing targeted datasets from evaluator failure modes to strengthen your model’s next iteration.