title: "LLM向けRAG: 検索拡張生成によるよりスマートなAI"

Retrieval-Augmented Generation (RAG) is an AI framework that enhances Large Language Models (LLMs) by connecting them to external, authoritative knowledge bases. The process involves retrieving relevant, up-to-date information in response to a user query and then feeding this context to the LLM to generate a more accurate and grounded response. Key benefits of RAG include improved accuracy, reduced model hallucinations, and increased user trust through the ability to cite sources. This method provides a cost-effective and scalable alternative to fine-tuning, as it allows models to access new information without the need for complete retraining.