The emergence of Large Language Models (LLMs) has improved software development efficiency, but their performance can be hindered by training data limitations and prompt design issues. Existing LLM development tools often operate as black boxes, with users unable to view the prompts used and unable to improve performance by correcting prompts when errors occur. To address the aforementioned issues, GPTutor was introduced as an open-source AI pair programming tool, offering an alternative to Copilot. GPTutor empowers users to customize prompts for various programming languages and scenarios, with support for 120+ human languages and 50+ programming languages. Users can fine-tune prompts to correct the errors from LLM for precision and efficient code generation. At the end of the paper, we underscore GPTutor's potential through examples, including demonstrating its proficiency in interpreting and generating Sui-Move, a newly introduced smart contract language, using prompt engineering.

翻译：大型语言模型（LLM）的出现提高了软件开发效率，但其性能可能受到训练数据限制和提示设计问题的制约。现有LLM开发工具通常以黑箱方式运行，用户既无法查看所使用的提示信息，也无法在出错时通过修正提示来改善性能。针对上述问题，GPTutor被提出作为一个开源AI结对编程工具，为Copilot提供了替代方案。GPTutor赋予用户针对不同编程语言和场景自定义提示的能力，支持120多种人类语言和50多种编程语言。用户可以微调提示以纠正LLM产生的错误，从而实现精准高效的代码生成。在论文末尾，我们通过示例强调了GPTutor的潜力，包括展示其利用提示工程解读和生成新型智能合约语言Sui-Move的能力。