Generative artificial intelligence and prompt engineering in asthma-related settings

Generative artificial intelligence is rapidly emerging as a valuable tool in medicine, with increasing use in asthma and allergy practices. Large language models (LLMs) can generate human-like text and synthesize clinical information, allowing clinicians to streamline workflows and summarize clinical documentation. In asthma care, artificial intelligence systems have demonstrated the ability to analyze patient data, assist in clinical decision support, and improve accessibility to educational resources for both clinicians and patients. As the use of LLMs expands in healthcare, understanding how to communicate effectively with these systems has become increasingly important. Prompt engineering refers to the process of designing structured instructions that guide LLMs to produce accurate and clinically relevant outputs. Effective prompts typically incorporate key components, such as a defined role or persona for the model, a clearly specified task, relevant clinical context, and instructions for output format or reading level. In asthma and allergy practice, these structured prompts can be used to support tasks such as summarizing clinical encounters, generating patient education materials, and assisting with clinical reasoning. Clinicians can also use different prompting strategies depending on the clinical objective, including open-ended prompts for exploratory tasks, focused prompts for targeted clinical questions, chained prompts for stepwise reasoning, and choice-based prompts for structured decision support. By understanding prompt design principles and choosing appropriate prompt types, clinicians can improve the precision and clinical relevance of LLM outputs.