DSPy Evaluation Harness (3.2.x)

The metric is usually more important than the program. For dspy.GEPA especially, the quality of textual feedback in your metric determines whether optimization converges.

Two rules

1. Return a dspy.Prediction(score=..., feedback=...), not a dict. dspy.Evaluate's parallel executor aggregates scores via sum, which breaks on dict outputs (TypeError: unsupported operand type(s) for +: 'int' and 'dict'). dspy.Prediction supports __float__/__add__ and is what GEPA's adapter natively unwraps. A bare float still works for pure dspy.Evaluate scoring, but GEPA needs the score+feedback pair.
2. Separate valset. Never optimize and evaluate on the same examples. Optimizers overfit fast.

Canonical rich-feedback metric

import dspy