<![CDATA[. Evaluating the performance of the best employees in a company is a common problem faced by the human resources division. Assessing subjectively and comprehensively is a challenge that must be overcome in building sustainable business growth. For organizations that are already able to manage employee assignment data in a structured and systematic form, this can be done by processing quantitative data obtained from the task management system. In this study, a large language model-based approach is proposed to identify the best employees based on assignment data for one year. A comparison was made of six versions of LLM on OpenAI to measure employee performance based on a predetermined prompting design. As a result, 50% of respondents considered that the criteria proposed by GPT4o-mini were more appropriate to their needs, and 60% of respondents considered that the employee ranking results produced by GPT-4 were more relevant to the reality.]]>
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