Building Reflective Prompt Optimization with GEPA: Multi-Component Prompts, Structured Feedback, and Held-Out Validation

Summary

<p>In this tutorial, we use GEPA as a reflective prompt-evolution framework to improve how a small language model solves multi-step arithmetic word problems. We start from a weak seed prompt, build a deterministic benchmark, and define a structured evaluator that returns actionable feedback. A multi-component setup evolves both the instruction field and the output-format rules together. We then compare the baseline and optimized prompts on a held-out validation set to check whether the gains generalize.</p> <p>The post <a href="https://www.marktechpost.com/2026/06/07/building-reflective-prompt-optimization-with-gepa-multi-component-prompts-structured-feedback-and-held-out-validation/">Building Reflective Prompt Optimization with GEPA: Multi-Component Prompts, Structured Feedback, and Held-Out Validation</a> appeared first on <a href="https://www.marktechpost.com">MarkTechPost</a>.</p>