Employee attrition is a critical issue faced by organizations, with significant costs associated with turnover and the loss of valuable talent. Traditional methods for predicting attrition often rely on statistical techniques that, while useful, struggle to capture the com- plexity of modern workforces. Recent advancements in machine learning (ML) have provided more accurate, scalable solutions, al- lowing organizations to analyze diverse data points and predict attrition with greater precision. However, the emergence of large language models (LLMs) has opened new possibilities in human resource management by offering the ability to interpret contextual information from employee communications and detect subtle cues related to turnover. In this paper, we leverage the IBM HR Analytics Employee At- trition dataset to evaluate the effectiveness of a fine-tuned GPT-3.5 model in comparison to traditional machine learning classifiers, including Logistic Regression, k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, AdaBoost, and XGBoost. Our study focuses on assessing the predictive power, interpretability, and real-world applicability of each model. While traditional models offer ease of use and transparency, LLMs have the potential to uncover more nuanced patterns in employee behavior. Through our analysis, we aim to provide practical insights for or- ganizations seeking to enhance their employee retention strategies with advanced predictive tools. Our results show that the fine-tuned GPT-3.5 large language model (LLM) outperforms traditional machine learning approaches in terms of prediction accuracy, with an impressive precision of 0.91, recall of 0.94, and an F1-score of 0.92. In contrast, the best-performing traditional model, Support Vector Machine (SVM), achieved an F1-score of 0.82, while ensemble methods like Random Forest and XGBoost reached F1-scores of 0.80. These findings highlight the ability of GPT-3.5 to capture complex patterns in employee behavior and attrition risks, offering enhanced interpretability by identifying subtle linguistic cues and recurring themes. This demonstrates the potential of integrating LLMs into HR strategies to significantly improve predictive performance and decision-making in employee retention.