Retrieval-Augmented Generation (RAG) and Fine-Tuning are two methods for tailoring Large Language Models (LLMs) to specific tasks or domains. RAG combines information retrieval with generative language models, while fine-tuning involves training a pre-trained LLM on a specific dataset. Both approaches have their strengths and weaknesses, and the best method depends on the specific requirements of your application. In many cases, a hybrid approach combining both techniques can yield optimal results. RAG is particularly useful for building chatbots over private knowledge sources, while fine-tuning is widely adapted to instruction tuning, code generation, and domain adaptation tasks.