  The advent of Large Language Models (LLMs) has ushered in a new era for
design science in Information Systems, demanding a paradigm shift in tailoring
LLMs design for business contexts. We propose and test a novel framework to
customize LLMs for general business contexts that aims to achieve three
fundamental objectives simultaneously: (1) aligning conversational patterns,
(2) integrating in-depth domain knowledge, and (3) embodying theory-driven soft
skills and core principles. We design methodologies that combine
domain-specific theory with Supervised Fine Tuning (SFT) to achieve these
objectives simultaneously. We instantiate our proposed framework in the context
of medical consultation. Specifically, we carefully construct a large volume of
real doctors' consultation records and medical knowledge from multiple
professional databases. Additionally, drawing on medical theory, we identify
three soft skills and core principles of human doctors: professionalism,
explainability, and emotional support, and design approaches to integrate these
traits into LLMs. We demonstrate the feasibility of our framework using online
experiments with thousands of real patients as well as evaluation by domain
experts and consumers. Experimental results show that the customized LLM model
substantially outperforms untuned base model in medical expertise as well as
consumer satisfaction and trustworthiness, and it substantially reduces the gap
between untuned LLMs and human doctors, elevating LLMs to the level of human
experts. Additionally, we delve into the characteristics of textual
consultation records and adopt interpretable machine learning techniques to
identify what drives the performance gain. Finally, we showcase the practical
value of our model through a decision support system designed to assist human
doctors in a lab experiment.
