Large language model (LLM) fine-tuning is a key technique for adapting pre-trained AI models to specific tasks or domains. Fine-tuning involves training an existing model on a new, task-specific dataset, updating its parameters to improve performance. This process balances improving capabilities with managing potential drawbacks like robustness degradation and catastrophic forgetting. Alternatives to fine-tuning, such as prompt engineering and Retrieval-Augmented Generation (RAG), offer different ways to customize LLMs, each with its own set of trade-offs regarding complexity, data integration, and privacy. Parameter-efficient fine-tuning (PEFT) methods like LoRA are emerging as promising approaches, offering efficiency and flexibility. The selection of a specific model and method should align with strategic goals, available resources, and the desired return on investment.