From ideas to ventures: building entrepreneurship knowledge with LLM, prompt engineering, and conversational agents

Entrepreneurship education has evolved to meet the demands of a dynamic business environment, necessitating innovative teaching methods to prepare entrepreneurs for market uncertainties. Large Language Models (LLMs) like the Generative Pre-trained Transformer 4 (GPT-4), recognized for their exceptional performance on public datasets, are examined in this study for their potential adaptability and interactivity nature, which align well with the dynamic nature of entrepreneurship learning. The interaction with LLMs can be enhanced by using effective prompt engineering techniques (PETs) that allow for crafting precise queries to elicit accurate and relevant responses for entrepreneurial learning. Critical concerns regarding the use of GPT-4 and conversational agents in entrepreneurship courses include the reliability and accuracy of data sources, the necessity for specific, real-time data for effective decision-making, and the lack of in-depth exploration of effective prompting strategies tailored to entrepreneurship education. Addressing these issues, this study aims to identify and compare the quality output of currently available PETs, develop innovative PETs that are well-aligned with entrepreneurial learning, and provide guidelines on how to fully utilize LLMs and conversational agents with Retrieval Augmentation Generation (RAG) technology in entrepreneurship education. The combination of conversational agents and RAG technology into a hybrid innovative approach overcomes inherent limitations in each technology separately and enhances efficiency and relevancy in entrepreneurship education through exact, dynamic interactions and advanced memory capabilities. The findings of the study significantly contribute to the field of entrepreneurship by offering practical insights for students and educators on enhancing the entrepreneurship learning experience, particularly by utilizing cutting-edge technology to improve data relevance and answer accuracy in entrepreneurial queries and scenarios.