Prompt Engineering for Large Language Models?

We harness Large Language Models (LLMs) to perform all kinds of tasks, from writing to problem-solving. That harnessing, however, is not necessarily intuitive.

Researchers at the University of Science and Technology of China (USTC) are developing practical strategies and guidelines for using LLMs. They have demonstrated that well-crafted prompts enhance the accuracy and relevance of responses, eliminating performance issues caused by low-quality instructions. They published their work in Nature Human Behavior.

LLMs can understand human language, but designing prompts for LLMs is challenging. Effective engagement with these user-friendly LLMs improves outputs, while poorly structured prompts result in inadequate answers.

The research highlights “prompt engineering,” a technique optimizing LLM output through accurate input control. Strategies include giving explicit instructions, relevant context, obtaining multiple options, etc. The methods reduce the compounding effect of errors.

The research serves as a practical guide for interactions with LLMs designed to achieve ideal outcomes and enhance understanding of their potential.