Unleashing the potential of prompt engineering: a comprehensive review

This comprehensive review explores the transformative potential of prompt engineering within the realm of large language models (LLMs) and multimodal language models (MMLMs). The development of AI, from its inception in the 1950s to the emergence of neural networks and deep learning architectures, has culminated in sophisticated LLMs like GPT-4 and BERT, as well as MMLMs like DALL-E and CLIP. These models have revolutionized tasks in diverse fields such as workplace automation, healthcare, and education. Prompt engineering emerges as a crucial technique to maximize the utility and accuracy of these models. This paper delves into both foundational and advanced methodologies of prompt engineering, including techniques like Chain of Thought, Self-consistency, and Generated Knowledge, which significantly enhance model performance. Additionally, it examines the integration of multimodal data through innovative approaches such as Multi-modal Prompt Learning (MaPLe), Conditional Prompt Learning, and Context Optimization. Critical to this discussion is the aspect of AI security, particularly adversarial attacks that exploit vulnerabilities in prompt engineering. Strategies to mitigate these risks and enhance model robustness are thoroughly reviewed. The evaluation of prompt methods is addressed through both subjective and objective metrics, ensuring a robust analysis of their efficacy. This review underscores the pivotal role of prompt engineering in advancing AI capabilities, providing a structured framework for future research and application.