How to Write Effective Generative AI Prompts for Family Medicine

The last several years have seen a surge in artificial intelligence (AI) integration in health care. From December 2023 to March 2025, the number of Food and Drug Administration-approved devices increased from 692 to 1,016, demonstrating the rapid growth of AI-enabled tools in less than 18 months.^1,2

Traditional machine learning models currently perform the majority of AI-driven tasks, especially in radiology. While these models typically handle specific, singular tasks or functions (e.g., reading x-rays), generative AI can perform a multitude of tasks simultaneously. Generative AI is already influencing medical practice outside the scope of diagnostics. It has tremendous potential to make medical practice more efficient, especially for physicians who know how to use it effectively.³ This article provides tips and best practices for writing prompts to get the most out of large language model (LLM) generative AI programs — general-purpose programs (such as ChatGPT, Gemini, Perplexity, Claude, or DeepSeek) or specialized platforms (such as OpenEvidence, Glass Health AI, or Doximity). We will review recommendations for prompting responsibly, discuss the “four Rs” for writing an effective prompt, describe advanced prompting techniques, and provide concrete examples of prompts that family physicians can use to support their daily work.

LLMs are powerful tools when used for the right indication and prompted appropriately. But they should be thought of as tools — just as any other modality in medicine — with their own set of indications, risks and benefits, ethical considerations, and costs. When designing a prompt, it is important to include what you want the LLM to do, but just as important to ask it to do so in a responsible way.

Avoiding bias. Because LLMs are often trained on data that may include racial, gender, or other types of bias, their output can also be biased. Responsible prompting should use inclusive language and avoid irrelevant descriptors (e.g., race, ethnicity, gender, sex, or sexuality). Avoid stereotypes or generalizations in a prompt, as the LLM will likely carry them through to the output.⁴ Additionally, avoid slang or colloquial phrases (e.g., “frequent flyers” for patients who often present to the emergency department or “zebras” for unusual diagnoses) because the model may take them literally or just find them confusing.