Explore the key differences and benefits of Retrieval Augmented Generation (RAG), fine-tuning, and prompt engineering in optimizing large language models. Learn how RAG enhances model performance by integrating external knowledge, while fine-tuning adapts models to specific tasks, and prompt engineering refines outputs through strategic input design. Discover the best practices and use cases for each approach to maximize the potential of large language models.