Prompt engineering for academic and clinical writing: A practical guide for clinicians

ABSTRACT

Large language models (LLMs) are increasingly integrated into clinical workflows, offering potential benefits ranging from literature synthesis and clinical decision support to patient education and documentation assistance. However, the quality of LLM output is highly dependent on the quality of the instructions provided, a process known as prompt engineering. This article introduces clinicians to the foundational principles and advanced techniques of prompt engineering, enabling clinicians to extract accurate, contextually relevant, and clinically safe responses. Key strategies discussed include role prompting, chain-of-thought reasoning, few-shot learning, and output constraint specification. The paper also addresses domain-specific considerations, including hallucination risk, the importance of clinical verification, and ethical obligations when using artificial intelligence-generated content in patient care contexts.