Prompt engineering plays a crucial role in unlocking the potential of large language model. This method guides the model’s response by designing prompt instructions to ensure the relevance, coherence, and accuracy of the response. Prompt engineering does not require fine-tuning model parameters and can be seamlessly connected with downstream tasks. Therefore, various prompt engineering techniques have become a research hotspot in recent years. Accordingly, this study introduces the key steps for creating effective prompts, summarizes basic and advanced prompt engineering techniques, such as chain of thought and tree of thought, and deeply explores the advantages and limitations of each method. At the same time, it discusses how to evaluate the effectiveness of prompt methods from different perspectives and using different methods. The rapid development of these technologies enables large language models to succeed in a variety of applications, ranging from education and healthcare to code generation. Finally, future research directions of prompt engineering technology are prospected.