An Empirical Study on the Effects of System Prompts in Instruction-Tuned Models for Code Generation
A 360-configuration study finds detailed prompts can hurt performance, and few-shot examples degrade larger models.
Deep Dive
Researchers Zaiyu Cheng and Antonio Mastropaolo published an empirical study analyzing how system prompts affect instruction-tuned models (ILMs) for code generation. Their 360-configuration experiment across four models found that more detailed prompts don't guarantee better code, few-shot examples can harm specialized models, and Java is more sensitive to prompts than Python. This means developers must carefully tailor prompts to the specific model and language.
Why It Matters
Engineers can write more effective prompts for tools like GitHub Copilot and Claude, improving code quality and developer productivity.