GEPA Optimization Codegen Rules (@ax-llm/ax)

Use this skill to generate GEPA optimization code. Prefer the top-level optimize(...) helper for normal code, and use direct AxGEPA / AxBootstrapFewShot only when the user needs low-level optimizer control.

Use These Defaults

• Use optimize(program, train, metric, { studentAI, teacherAI, ... }) for normal generator and flow tuning.
• Prefer ai(), ax(), and flow() for new code.
• Use a strong teacherAI and a cheaper studentAI.
• Pass validationExamples when you have a holdout set.
• Set maxMetricCalls to bound optimizer cost; optimize(...) defaults it to 100.
• Use scalar metrics for one objective and object metrics for Pareto optimization.
• Apply results with program.applyOptimization(result.optimizedProgram!).
• For tree-wide runs, expect optimizedProgram.componentMap.
• Persist artifacts with axSerializeOptimizedProgram(...) and restore them with axDeserializeOptimizedProgram(...) so the same flow works in browsers and Node.
• optimize(...) runs AxBootstrapFewShot -> AxGEPA for small starter sets by default, preserving the demos in result.optimizedProgram.demos.

Critical Rules