Intelligent Q&A application of rules and regulations based on large models and RAG technology

Employees face high learning costs and inefficient retrieval due to the increasing volume and complexity of company rules and regulations. This paper proposes an intelligent Q&A system using OCR, vectorization, Large Language Models (LLM), and Retrieval-Augmented Generation (RAG). We detail the knowledge base construction, prompt engineering, and Q&A module design. Example validation demonstrates the system’s accuracy, completeness, and logical output. Results show it significantly improves employee learning efficiency, offering an innovative solution for rules & regulations informatization.