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Automated Prompt Engineering For Cost-effective Code Generation Using Evolutionary Algorithm

Abstract

Large Language Models have seen increasing use in various software development tasks, especially in code generation. The most advanced recent methods attempt to incorporate feedback from code execution into prompts to help guide LLMs in generating correct code in an iterative process. While effective, these methods could be costly due to numerous interactions with the LLM and extensive token usage

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