Prompt engineering has burgeoned into a pivotal technique for augmenting the capabilities of large language models (LLMs) and vision-language models (VLMs), utilizing task-specific instructions or prompts to amplify model efficacy without altering core model parameters. These prompts range from natural language instructions that provide context to guide the model to learning vector representations that activate relevant knowledge, fostering success in myriad applications like question-answering and commonsense reasoning. Despite its burgeoning use, a systematic organization and understanding of the diverse prompt engineering methods still need to be discovered.

This survey by researchers from the Indian Institute of Technology Patna, Stanford University, and Amazon AI endeavors to bridge this gap by offering a structured overview of the recent advancements in prompt engineering, categorized by application area. It meticulously analyzes over 29 distinct techniques, delving into their methodologies, applications, models involved, and datasets utilized. This examination extends from foundational methods like zero-shot and few-shot prompting to more intricate approaches such as chain of code prompting, showcasing the field’s breadth and depth.

The survey highlights the transformative impact of prompt engineering on the adaptability of LLMs and VLMs, enabling these models to excel across diverse tasks and domains with a finesse previously unattainable through traditional model training paradigms. Prompt engineering pushes the boundaries of AI by sidestepping the need for model retraining or extensive fine-tuning, paving the way for a future teeming with possibilities.

The survey underscores the importance of prompt engineering in steering model responses, thus enhancing the adaptability and applicability of LLMs across various sectors. It presents a comprehensive taxonomy and summarizes key points, datasets, models, and the critical features of each prompting technique, providing a clearer understanding of this rapidly developing field. This systematic analysis aims to illuminate open challenges and opportunities for prompt engineering, facilitating future research in this dynamic arena.

In conclusion, the domain of artificial intelligence witnesses prompt engineering as a transformative force, unlocking the vast potential of LLMs. This survey serves as a foundational resource, categorizing distinct prompt engineering techniques based on their functionalities, inspiring further research, and empowering innovators in the evolving landscape of prompt engineering. Despite its successes, challenges such as biases, factual inaccuracies, and interpretability gaps persist, necessitating continued investigation and mitigation strategies. With emerging trends like meta-learning and hybrid prompting architectures, the future of prompt engineering holds immense potential, yet ethical considerations remain paramount to ensure its responsible development and deployment.

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