Fine-tuning a large language model (LLM) is crucial for tailoring pre-trained models to perform specific tasks with higher precision. LLMs like GPT, initially trained on extensive datasets, excel at understanding and generating human-like text, but their broad training often lacks the specificity needed for specialized applications. Fine-tuning addresses this by further training these pre-trained models on domain-specific datasets, refining the model's capabilities and enhancing its performance in tasks such as sentiment analysis, question answering, and document summarization. The process refines the model's parameters to adapt to new scenarios without altering its original parameters, preserving the LLM's general knowledge and preventing catastrophic forgetting during new task learning. Fine-tuning is particularly effective at teaching an LLM domain expertise or adapting it to a specific tone or style of communication.