Retrieval-Augmented Generation (RAG) chatbots show promise in educational settings, yet their application in industrial design, with its iterative and reflective workflows, remains underexplored. This study investigates how master’s students in industrial design perceive the effectiveness of a RAG chatbot in supporting their graduation projects. We developed a chatbot prototype trained on 132 industrial design theses (2021–2023), employing semantic search, multimodal capabilities, and stage-specific guidance, and evaluated it through a mixed-methods approach involving a quantitative question-ranking task (n=7) and a qualitative focus group (n=4). Findings indicate strong performance for practical, early-stage queries but highlight issues with irrelevant corpus results, verbose outputs, and underused features, with five key themes emerging: corpus relevance, output reliability, interaction clarity, multimodal support, and experience-oriented learning. These results inform design guidelines for behaviorally aligned RAG chatbots, enhancing support for critical thinking and process navigation in industrial design education.