DSPy GEPA Optimizer (3.2.x)

GEPA (Genetic-Pareto) is a reflective optimizer: it mutates a program's instructions and few-shots using an LM that reads your metric's textual feedback and proposes improvements. It maintains a Pareto frontier across validation tasks and is the default recommendation for complex DSPy workloads in 2026.

The expansion "Genetic-Evolutionary Prompt Adaptation" that appears in some AI-generated summaries is an LLM-hallucinated backronym. The paper defines GEPA as Genetic-Pareto; the "Pareto" is load-bearing (GEPA keeps a frontier of candidates rather than collapsing to one).

Prerequisites — do these first or GEPA wastes rollouts

1. A dspy.Module that runs end-to-end (see dspy-fundamentals).
2. A rich-feedback metric returning dspy.Prediction(score=float, feedback=str) (see dspy-evaluation-harness). A float-only metric makes GEPA no better than MIPRO. A dict with the same fields still crashes dspy.Evaluate under DSPy 3.2.1 — use dspy.Prediction.
3. trainset and a separate valset. For GEPA, maximize training examples and keep validation just large enough to represent the downstream distribution; do not reuse the same examples for both.
4. A reflection_lm — a strong LM (often the same or stronger than the task LM) set to temperature=1.0 for creative proposals. Current DSPy docs use a GPT-5-class reflection model with a large output budget.

Canonical call

import dspy