title: RAGとLLMの微調整：企業に最適なものを見つける

Retrieval-Augmented Generation (RAG) and fine-tuning are two primary methods for adapting Large Language Models (LLMs) for specific enterprise use cases. RAG provides external, up-to-date context to the model during inference, which is useful for knowledge-intensive tasks. In contrast, fine-tuning adjusts the model's internal parameters to specialize its behavior or style based on a specific dataset. For optimal performance, a hybrid approach combining both RAG and fine-tuning can deliver more accurate and personalized AI solutions.