Dspy Gepa Optimizer

GEPA is DSPy's reflective optimizer that reads your metric's textual feedback to evolve prompts and few-shots across a Pareto frontier. If your metric returns rich critiques (not just a float), GEPA beats MIPROv2 on complex tasks with fewer rollouts. The catch: you need a metric that returns dspy.Prediction(score=float, feedback=str), a strong reflection LM at temperature 1.0, and separate train/val splits where you actually maximize training data instead of the usual validation-heavy split. Use auto="medium" for everyday work, auto="heavy" before shipping. The feedback quality is the load-bearing part: vague critiques waste rollouts, specific ones about why it failed and what good looks like are what the reflection LM acts on.

Install to Claude Code

npx -y skills add intertwine/dspy-agent-skills --skill dspy-gepa-optimizer --agent claude-code