We introduce ChatSQC, an innovative chatbot system that combines the power of OpenAI's Large Language Models (LLM) with a specific knowledge base in Statistical Quality Control (SQC). Our research focuses on enhancing LLMs using specific SQC references, shedding light on how data preprocessing parameters and LLM selection impact the quality of generated responses. By illustrating this process, we hope to motivate wider community engagement to refine LLM design and output appraisal techniques. We also highlight potential research opportunities within the SQC domain that can be facilitated by leveraging ChatSQC, thereby broadening the application spectrum of SQC. A primary goal of our work is to equip practitioners with a tool capable of generating precise SQC-related responses, thereby democratizing access to advanced SQC knowledge. To continuously improve ChatSQC, we ask the SQC community to provide feedback, highlight potential issues, request additional features, and/or contribute via pull requests through our public GitHub repository. Additionally, the team will continue to explore adding supplementary reference material that would further improve the contextual understanding of the chatbot. Overall, ChatSQC serves as a testament to the transformative potential of AI within SQC, and we hope it will spur further advancements in the integration of AI in this field.

翻译：我们提出了ChatSQC，一个创新性的聊天机器人系统，它将OpenAI大型语言模型（LLM）的强大能力与统计质量控制（SQC）特定知识库相结合。本研究聚焦于使用特定SQC参考文献增强LLM，揭示了数据预处理参数与LLM选择如何影响生成响应的质量。通过阐释这一过程，我们期望激发更广泛的社区参与，以优化LLM设计与输出评估技术。同时，我们指出了SQC领域内借助ChatSQC可促进的潜在研究机遇，从而拓展SQC的应用范围。本研究的核心目标是为实践者提供能够生成精准SQC相关响应的工具，进而普及高级SQC知识的可及性。为持续改进ChatSQC，我们邀请SQC社区通过我们的公共GitHub库提供反馈、指出潜在问题、请求新增功能，或通过拉取请求贡献代码。此外，团队将持续探索添加补充参考材料，以进一步提升聊天机器人的上下文理解能力。总体而言，ChatSQC见证了人工智能在SQC领域的变革潜力，我们期望它能推动该领域AI整合的进一步突破。