Today, following our discussion on LLM Orchestration, we are specifically introducing the RAG Pipeline. For satisfactory processing, the RAG (Retrieval-Augmented Generation) pipeline is a key element in building AI systems that provide successful and context-aware answers. This pipeline combines the powerful capabilities of language models with document-related search functions, ensuring that AI responses are based on user data rather than relying solely on prior knowledge. The following is a subsequent diagram illustrating the RAG pipeline. It shows how data is retrieved, processed, and used to generate high-quality, powerful answers. This approach not only enables excellent answers but also allows for the integration of features through added content. We welcome any questions related to software, including issues encountered during the learning and development process. Our goal is ```for the future```.