Prompt Design

Prompt design focuses on crafting effective instructions for large language models (LLMs) to maximize their performance on various tasks, ranging from text annotation and question answering to code generation and even scientific primer design. Current research emphasizes automating prompt creation, exploring different prompt structures (e.g., chain-of-thought prompting), and analyzing the impact of prompt features (e.g., length, style, inclusion of examples) on LLM accuracy and compliance across diverse model architectures (including GPT-3/4, LLaMA, PaLM2). This field is crucial for unlocking the full potential of LLMs, improving efficiency in data annotation and other computationally intensive tasks, and enabling more reliable and interpretable results across numerous scientific and practical applications.