ChatGPT, powered by the Generative Pre-trained Transformer (GPT) language model, has sparked a revolution in AI applications. However, it lacks specialized knowledge and faces limitations around private data use. To overcome these challenges, organizations can fine-tune LLMs like GPT with their distinct workflows and proprietary or private data. Fine-tuning involves taking a pre-trained base LLM and further training it on a specialized dataset for a particular task or knowledge domain. This process includes setting up the development environment, choosing a model to fine-tune, preparing datasets, uploading training datasets, creating a fine-tuning job, checking the status of the model during fine-tuning, accessing the fine-tuned model, accessing model checkpoints, and improving the model. Fine-tuning can significantly enhance the efficacy of generative AI applications when applied correctly.