Exploring Techniques and Applications of Prompt Engineering for Large Language Models

Prompt engineering has become an effective approach for improving the performance of large language models (LLMs) and vision–language models (VLMs) without modifying their internal architecture. By designing precise instructions, structured formats, and illustrative examples, prompts guide models to interpret tasks more accurately and generate more reliable outputs. Despite its rapid growth and significant impact on areas such as question answering, reasoning, and multimodal tasks, the field still lacks a well-organized overview of its diverse techniques. This paper presents a structured survey of recent advances in prompt engineering, explaining how each technique functions, the tasks it supports, the models and datasets used for evaluation, and the benefits and limitations observed. Visual summaries, including tables and diagrams, are provided to enhance understanding. Overall, this review offers a comprehensive introduction to prompt engineering and outlines key challenges and promising directions for future research.