From prompt engineering to generative artificial intelligence for large models: the state of the art and perspective

Abstract

Large language models and vision-language models have demonstrated significant potential in various downstream applications, making it become a research hotspot. However, the issues such as hallucinations and knowledge transfer impact the performance of these models. Firstly, this paper explores the fundamental principles of prompt engineering and alignment techniques, and proposes the concept of "prescriptive", which is based on optimizing prompts and expert feedback verification and can be adjusted in real-time. This aims to further enhance the performance of large language models in cross-domain applications. Secondly, the core technologies of prompt engineering, such as the principles of multi-step reasoning for handling complex tasks, are analyzed in depth. Additionally, the development status of prompt engineering is discussed based on practical applications in various fields. Finally, this paper summarizes the challenges faced by prompt engineering and looks into its future development directions. The development of prompt engineering in theory and application, provide comprehensive solutions for improving the performance of large models in practical applications.

Keywords

• prompt engineering;alignment technology;generative artificial intelligence;large language models;vision-language models