ax-go-refine

To improve Go Ax generation outputs with evaluator feedback, you build reward-scored workflows that evaluate candidate outputs and feed the results into an optimizer loop to iteratively regenerate better candidates. This replaces ad hoc prompt handling with structured evaluator-guided iteration.

GEPA optimization in Go Ax workflows is an optimizer-backed generation approach using optimizer engine APIs to improve model outputs. It applies when a standalone refine helper is unavailable, enabling few-shot bootstrapping and iterative candidate improvement within the generated Go package.

Yes, the Go Ax package supports few-shot bootstrapping for refinement through its optimizer-backed generation APIs. You can use these optimizer engines to bootstrap few-shot examples and iteratively refine candidates based on evaluator scoring.

To align your Go Ax refinement code with the generated package API, you rewrite your code to follow generated package examples, use evaluator feedback correctly, and apply the appropriate optimizer API. This ensures your code matches the native package surface and capability manifests.

You should use optimizer-backed generation in Go Ax when a standalone refine helper is not available for your specific task. This approach leverages GEPA, few-shot bootstrapping, and optimizer engine APIs to achieve reward-scored candidate improvement.