A Current Revision of Prompt Engineering in Business Operations

Abstract

Prompt engineering in the context of operations emerges as a key discipline to optimise artificial intelligence (AI) models for specific operational tasks. Considering the importance of this field, our group offers a current review of designed prompts using language models in business operations. At this stage, the study focuses on confirming the advancement of prompts in response to their precise formulation and applicability in real-world scenarios and different engineering approaches. We use the gold mining problem to evaluate prompt techniques such as Few-shot, Chain-of-thought, and Tree-of-thoughts (ToT) in LLMs. The results show the importance of adapting the prompts to the type of technique and the characteristics of the problem at hand. Our research also offers theoretical and practical foundations for their integration with AI models, highlighting the importance of prompt engineering to enhance automation and decision-making in business environments.