Building Reflective Prompt Optimization with GEPA: Multi-Component Prompts, Structured Feedback, and Held-Out Validation
Quick Answer
This tutorial demonstrates the use of GEPA as a reflective prompt-evolution framework to enhance a small language model's ability to solve multi-step arithmetic word problems.
Quick Take
By evolving both instruction and output formats through structured feedback, the study compares baseline and optimized prompts on a held-out validation set to assess generalization of performance improvements.
Key Points
- GEPA framework improves small language model performance on arithmetic word problems.
- A deterministic benchmark was established from a weak seed prompt.
- Structured feedback was implemented to provide actionable insights.
- Multi-component prompts evolved both instructions and output formats.
- Performance gains were validated against a held-out dataset.
Source Excerpt
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.
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