gepa-demo

SKILL.md

Prompt optimization is the process of improving the quality of prompts used in language models. It is often done manually, but increasingly their are frameworks (such as DSPy) being used to use LLMs to do this.

In essence, the process involves the user providing a dataset and a grader or reward model to judge an LLM's output. A prompt's performance on the dataset is measured, the gaps in in its performance identified, and a new prompt is then proposed and tested. This runs in a loop until an end state is reached.

GEPA (which stands for GEnetic PAreto) is a prompt optimization algorithm that follows the process above. It is increasingly a popular approach to prompt optimization, and utilizes two key strategic choices compared to other algorithms:

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