  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 provide a template and proof-of-concept on how LLMs can be
utilized by our community. 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.
