Fine-tuning is an essential technique for adapting pre-trained neural networks to specific tasks, optimizing their performance by training them on a smaller, task-specific dataset. This method is particularly significant in building large language model (LLM) applications, offering benefits such as improved accuracy, relevance, reduced development costs, and enhanced data efficiency. While fine-tuning allows for customization and gives a competitive edge by tailoring models to unique use cases, it involves more complexity and expertise compared to prompt engineering, which is often suggested as a preliminary approach. In scenarios where communicating desired outcomes through examples is easier, or when cost and latency are critical, fine-tuning becomes advantageous. Additionally, the blog discusses Retrieval Augmented Generation (RAG), which integrates external data to improve model performance, suggesting RAG as an initial step before considering fine-tuning for further optimization. The process of fine-tuning involves selecting appropriate pre-trained models, preparing high-quality datasets, configuring fine-tuning parameters, and evaluating the model's performance. Humanloop's platform facilitates the fine-tuning process by leveraging customer data, providing tools for data collection, and offering support for fine-tuning directly on their platform.