Towards Using Prompt Engineering in Large Language Models to Assist Decision Making

Generative AI and large language models (LLMs) offer a new type of decision support that permits the user to interact with the system using natural language. LLMs are particularly useful for pragmatic reasons. While syntax and semantics have been easier to handle over the years, pragmatics has been difficult to manage, at least before the invention of LLMs. Users interact with the system through one or more prompts to obtain a response. ‘Prompt engineering’ is the art and science of designing and refining effective prompts to elicit the desired output from the LLM. Prompt engineering suggests techniques to design inputs, acquire data and information, and grasp the capabilities and limitations of the models. Prompts contain questions or instructions which are trimmed to optimize the prompts and obtain the desired answers, i.e., changing different parameters to extract the required data. These techniques range from trivial prompts to knowledge-intensive prompts. This paper explores prompt engineering directed towards supporting human decision making and suggests a framework for using LLMs towards decision goals. An overview of prompting techniques is provided with benefits and drawbacks along with examples. In addition, an overview of decision support is presented with a focus on the intelligence, design, choice and implementation paradigm. The paper proposes an LLM/DM framework for prompting for decision support with current LLMs and presents an empirical example.