Prompt Engineering for Large Language Models: A Systematic Review and Future Directions

The rapid evolution of large language models (LLMs) has significantly transformed various domains within artificial intelligence (AI) and natural language processing (NLP). Despite their widespread adoption, the discipline of prompt engineering, which is fundamental to maximizing the potential of LLMs, remains insufficiently explored. This systematic review aims to bridge this gap by critically analyzing existing methodologies, identifying prevailing challenges, and outlining prospective research directions. A thorough examination of literature indexed in ACM, IEEE Xplore, and SpringerLink, covering publications from 2018 to 2024, underscores the absence of standardized frameworks in prompt design, considerable variability in prompt effectiveness across diverse applications, and ethical concerns related to bias and model interpretability. To address these challenges, this study advocates for the development of adaptive prompt optimization techniques, reinforcement learning-driven prompt refinement, and the incorporation of explainable AI frameworks. The insights presented in this review provide a comprehensive perspective on the current state of prompt engineering and offer valuable recommendations to guide future advancements in AI and NLP research.