Supervised fine-tuning for large language models (LLMs) is a transfer learning technique where a pre-trained model is further refined using a curated dataset with labeled examples to enhance its performance on specific tasks. This process, which adjusts the model's parameters to specialize in tasks like text summarization, domain adaptation, and tone alignment, is more resource-efficient than training a model from scratch, requiring only a pre-trained model, a computer, and a small dataset. The supervised fine-tuning workflow involves curating a high-quality dataset, selecting an appropriate pre-trained model, implementing a training loop to adjust the model's weights, and evaluating the model's performance. Despite challenges like ensuring dataset quality and mitigating the risk of catastrophic forgetting, supervised fine-tuning offers a practical approach to tailoring LLMs for specialized tasks. A step-by-step tutorial demonstrates the process using DistilGPT2 to generate e-commerce product descriptions, highlighting the importance of dataset quality and the potential for fine-tuning to improve model outputs.