TY  - JOUR
T1  - Research on Prompt Engineering Techniques in Large Language Models
AU  - Son, Minjun 
AU  - Lee, Sungjin 
JO  - The Journal of Korean Institute of Communications and Information Sciences
PY  - 2025
DA  - 2025/1/1
DO  - 10.7840/kics.2025.50.1.9
KW  - Large Language Model
KW  - Prompt Engineering
KW  - In-context learning
KW  - Chain of Thought
KW  - Retrieval-Augmented Generation
AB  - Recent natural language processing technology has been advancing at an unprecedented pace, driven by the development of large language models. However, the issue of hallucination, where the model generates inaccurate or nonsensical responses, remains a challenge to be addressed. This paper analyzes various prompt engineering techniques in large-scale language models and derives prompt engineering methods that can achieve optimal response performance for each dataset. The study found that the most suitable prompt engineering techniques can vary depending on the characteristics of each dataset.