Fine-tuning large language models (LLMs) is a critical step in making them effective, efficient, and relevant in real-world applications. This process involves re-training pre-trained or foundational models on specific datasets to adapt to the context of the domain in question. Fine-tuning can help increase accuracy, provide personalized responses, and reduce the risk of undesired outputs. Various fine-tuning approaches exist, including transfer learning, sequential fine-tuning, task-specific fine-tuning, multi-task fine-tuning, and parameter-efficient fine-tuning. The Nebula LLM has been fine-tuned using a two-step training and fine-tuning process, utilizing unsupervised learning on a large text corpus followed by supervised task-specific and multi-task fine-tuning. Effective fine-tuning requires representative training data and can be challenging in enterprise settings due to the availability of confidential conversational data. Fine-tuning is a reliable approach for obtaining higher output accuracy and quality, but alternate techniques such as prompt engineering and retrieval augmented generation (RAG) may be considered due to lower implementation complexity and associated costs.