  This study aims to improve knowledge-based question-answering (QA) systems by
overcoming the limitations of existing Retrieval-Augmented Generation (RAG)
models and implementing an advanced RAG system based on Graph technology to
develop high-quality generative AI services. While existing RAG models
demonstrate high accuracy and fluency by utilizing retrieved information, they
may suffer from accuracy degradation as they generate responses using
pre-loaded knowledge without reprocessing. Additionally, they cannot
incorporate real-time data after the RAG configuration stage, leading to issues
with contextual understanding and biased information. To address these
limitations, this study implemented an enhanced RAG system utilizing Graph
technology. This system is designed to efficiently search and utilize
information. Specifically, it employs LangGraph to evaluate the reliability of
retrieved information and synthesizes diverse data to generate more accurate
and enhanced responses. Furthermore, the study provides a detailed explanation
of the system's operation, key implementation steps, and examples through
implementation code and validation results, thereby enhancing the understanding
of advanced RAG technology. This approach offers practical guidelines for
implementing advanced RAG systems in corporate services, making it a valuable
resource for practical application.
