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TechPulse take: This tutorial demonstrates a deterministic framework for evolving prompts using structured feedback and held-out validation to improve small language models on multi-step arithmetic tasks. The approach highlights the importance of separating instruction fields from output-format rules while managing API call budgets during the optimization process. For practitioners, the key takeaway is the utility of programmatically generated benchmarks to ensure reliable evaluation of prompt generalization beyond the training set.

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

MarkTechPostJune 7, 20264 min read

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.

In this tutorial, we use GEPA as a reflective prompt-evolution framework to improve the way a language model solves arithmetic word problems. We begin with a weak seed prompt, create a small deterministic benchmark, define a structured evaluator, and pass actionable feedback to GEPA so it can understand why a candidate prompt fails. We also use…

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