Work in Progress: Unlocking Code Generation Through Synergistic Prompt Engineering
Kin-Hon Ho, Michael Georgiades, Tsz-Kin Justin Fan, Yun Hou, Ken C. K. Fong, Tse-Tin Chan · 2025
Prompt engineering is crucial for optimizing large language models in code generation. This paper explores a synergistic prompt engineering approach that integrates complementary prompting techniques for solving programming problems. Preliminary experiments show that by leveraging the strengths of various prompting techniques, our synergistic approach significantly outperforms traditional single- prompting techniques, improving the accuracy of code generation for Python and C++ exercises. These findings suggest that our synergistic approach is a valuable tool for students, enhancing their interactions with large language models and improving AI-driven programming education.