邁向可信賴工業AI：應用檢索增強生成RAG消除非結構化數據推理中的幻覺

隨著生成式人工智慧快速發展，大型語言模型已展現強大的文本理解與生成能力。然而，這項技術在工業應用中仍面臨「幻覺」的核心挑戰—即模型可能產生看似合理但缺乏事實依據的回應，這對於精密工程應用來說是非常嚴重的缺點。本文探討檢索增強生成技術如何成為實現可信賴工業AI（Artificial Intelligence）的關鍵路徑，並介紹工研院機械所開發的「具重複自我反思的瑕疵檢測方法推薦模組」，讓語言模型的生成精確度提升至80%、上下文關聯性達85%。RAG（Retrieval-Augmented Generation） 不僅是技術創新，更代表工業AI從「技術展示」邁向「可信賴應用」的典範轉移，是實現可信賴工業AI的必經之路。

With the rapid development of generative artificial intelligence, large language models have demonstrated powerful text understanding and generation capabilities. However, this technology faces a core challenge in industrial applications—"hallucination"—where models may generate responses that appear reasonable but lack factual basis, which poses a serious problem for precision industries. This article explores how Retrieval-Augmented Generation (RAG) technology has become a critical path toward achieving trustworthy industrial AI, and introduces the "Defect Detection Method Recommendation Module with Iterative Self-Reflection" developed by ITRI, which improves the language model's generation accuracy to 80% and contextual relevance to 85%. RAG is not merely a technological innovation, but represents a paradigm shift in industrial AI from "technology demonstration" to "trustworthy application"—an essential pathway toward achieving trustworthy industrial AI.