Code Prompt

Code prompting is a technique that uses code-like structures within prompts to guide large language models (LLMs), particularly those trained on both text and code, towards improved performance on various tasks, including code generation and reasoning. Current research focuses on understanding how different prompt structures (e.g., chain-of-thought, structured prompts) affect the accuracy and reliability of LLM-generated code, as well as exploring the effectiveness of code prompting across diverse tasks beyond code generation, such as taxonomy expansion and conditional reasoning. This research is significant because it addresses the critical need for reliable and efficient code generation tools, while also shedding light on the underlying mechanisms of reasoning in LLMs and improving their usability in educational and professional settings.