Prompt engineering and framework: implementation to increase code reliability based guideline for LLMs

• URL: http://arxiv.org/abs/2506.10989v1
• Date: Wed, 19 Mar 2025 18:33:08 GMT
• Title: Prompt engineering and framework: implementation to increase code reliability based guideline for LLMs
• Authors: Rogelio Cruz, Jonatan Contreras, Francisco Guerrero, Ezequiel Rodriguez, Carlos Valdez, Citlali Carrillo,
• Abstract summary: We introduce a prompt template designed to improve the quality and correctness of generated code snippets.<n>We demonstrate that our approach outperforms widely studied zero-shot and Chain-of-Thought (CoT) methods in terms of the Pass@k metric.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a novel prompting approach aimed at enhancing the ability of Large Language Models (LLMs) to generate accurate Python code. Specifically, we introduce a prompt template designed to improve the quality and correctness of generated code snippets, enabling them to pass tests and produce reliable results. Through experiments conducted on two state-of-the-art LLMs using the HumanEval dataset, we demonstrate that our approach outperforms widely studied zero-shot and Chain-of-Thought (CoT) methods in terms of the Pass@k metric. Furthermore, our method achieves these improvements with significantly reduced token usage compared to the CoT approach, making it both effective and resource-efficient, thereby lowering the computational demands and improving the eco-footprint of LLM capabilities. These findings highlight the potential of tailored prompting strategies to optimize code generation performance, paving the way for broader applications in AI-driven programming tasks.

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