做提示工程和模型微调的开发者，GEPA 框架提供了一种系统化的提示优化方法，比手动调参更高效，值得尝试。
本文介绍使用 GEPA 作为反思式提示进化框架，优化小型语言模型解决多步算术应用题的能力。从弱种子提示开始，构建确定性基准和结构化评估器，返回可操作反馈。多组件设置同时进化指令字段和输出格式规则。最后在保留验证集上比较基线和优化提示，检验泛化能力。该方法展示了如何通过结构化反馈和验证集提升提示质量。
Building Reflective Prompt Optimization with GEPA: Multi-Component Prompts, Structured Feedback, and Held-Out Validation
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. The post Building Reflective Prompt Optimization with GEPA: Multi-Component Prompts, Structured Feedback, and Held-Out Validation appeared first on MarkTechPost .