近年來，隨著人工智慧技術的快速發展，大型語言模型（LLMs）在自然語言理解與生成方面展現出卓越能力，但其生成內容仍可能出現「幻覺」（hallucination）現象，導致資訊不準確或不一致，限制其在企業知識管理與決策支援中的實用性。本研究以 D 公司內部知識應用 為背景，探討檢索強化生成（Retrieval-Augmented Generation，RAG）技術在智慧問答系統中的應用，並分析企業內部資訊特性與查詢模式，以作為 RAG 技術應用的理論基礎。研究重點在於探討 RAG 技術如何結合語言模型與知識檢索機制，改善資訊落差、提升語意理解能力，並提供即時且可靠的回應。透過理論分析與模擬查詢情境的評估，本研究說明 RAG 技術在提升查詢準確率、改善使用者信任及優化知識管理效率上的潛力，為企業導入 AI 智慧問答系統提供可行的理論與實務參考。 關鍵詞：檢索強化生成、語言模型、智慧問答系統、向量資料庫、企業知識管理、D 公司內部知識 ;In recent years，with the rapid development of artificial intelligence，large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However，these models may produce “hallucinations,” resulting in inaccurate or inconsistent information，which limits their applicability in enterprise knowledge management and decision support. This study focuses on internal knowledge applications within D Company，investigating the use of Retrieval-Augmented Generation (RAG) technology in intelligent question-answering systems and analyzing the characteristics of enterprise knowledge and query patterns to provide a theoretical foundation for RAG application. The research emphasizes how RAG technology integrates language models with knowledge retrieval mechanisms to reduce information gaps，enhance semantic understanding，and deliver timely and reliable responses. Through theoretical analysis and evaluation in simulated query scenarios，this study demonstrates the potential of RAG technology to improve query accuracy，strengthen user trust，and optimize knowledge management efficiency，offering practical and theoretical guidance for enterprises adopting AI-based question-answering systems. Keywords: Retrieval-Augmented Generation，Language Model，Question-Answering System，Vector Database，Enterprise Knowledge Management，D Company Internal Knowledge
