Enhancing Prompt Optimization with GEPA: A Comprehensive Approach

This guide focuses on utilizing GEPA as a framework to enhance the performance of language models in solving arithmetic word problems. We initiate the process with a basic seed prompt and construct a deterministic benchmark to assess performance. A structured evaluator is defined to provide actionable feedback to GEPA, helping it identify the shortcomings of candidate prompts. The approach incorporates a multi-component prompt system, allowing both the instructional and output format elements to evolve concurrently. Ultimately, we evaluate the baseline prompt against the optimized version using a held-out validation set, revealing the improvements achieved through this evolutionary method. To begin, GEPA and LiteLLM are installed, followed by the configuration of task and reflection models. The OpenAI API key is securely set up, and two models are established: one for solving the problems and another for refining prompts. A deterministic dataset of arithmetic problems is generated, ensuring reliable evaluation. The evaluator assesses candidate prompts, providing feedback based on the accuracy and formatting of the model's responses. Finally, the optimization process is executed, aiming to enhance the prompt's effectiveness in solving complex arithmetic problems accurately and consistently.