Prompting Strategy

Prompting strategy research focuses on optimizing how instructions are given to large language models (LLMs) and other AI systems to elicit desired outputs, improving performance on various tasks. Current research explores diverse prompting techniques, including few-shot learning, chain-of-thought prompting, and narrative prompting, often applied to transformer-based models like GPT-4 and CLIP, to enhance accuracy, reduce biases, and control output characteristics. This field is crucial for unlocking the full potential of LLMs across diverse applications, from medical report generation and image quality assessment to more efficient text re-ranking and improved performance in challenging algorithmic tasks. The development of effective prompting strategies is essential for making these powerful models more reliable, efficient, and accessible.