Intelligent Q&A application of rules and regulations based on large models and RAG technology
Year: 2025
Type: article
Abstract: 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.
Source: Jisuanji shenghuojia.
DOI: 10.54097/jb36az85
Authors: Shugang Liu, Yu Zhao
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414428277