Prompting Strategies for Large Language Models in Primary Care: A Primer for Clinician-Artificial Intelligence...

Area of Science:

• Artificial Intelligence in Medicine
• Clinical Informatics
• Human-Computer Interaction

Background:

• Primary care involves extensive information synthesis, documentation, and communication.
• Large language models (LLMs) offer potential to streamline primary care workflows and reduce administrative tasks.
• LLMs, while promising, present challenges like inconsistent outputs and potential inaccuracies.

Purpose of the Study:

• To explore the role of prompt engineering in enhancing LLM performance for primary care.
• To provide guidance on designing effective prompts for clinical applications of LLMs.
• To emphasize the importance of critical appraisal of LLM outputs in patient care.

Main Methods:

• Discusses prompt engineering as an iterative process of task definition, LLM generation, and clinician refinement.
• Highlights the benefits of structured prompting frameworks (e.g., role, context, task, format).
• Mentions specific frameworks like Ask-Context-Expectation and PICO+O for tailored applications.

Main Results:

• High-quality prompts, characterized by specificity and context, yield superior LLM responses compared to vague prompts.
• Structured prompting significantly improves LLM output consistency and relevance.
• Thoughtful prompt design can mitigate LLM limitations such as inaccuracies and hallucinations.

Conclusions:

• Prompt engineering is an essential skill for leveraging LLMs effectively in primary care.
• LLMs should be utilized as cognitive assistants, not knowledge authorities, complementing human judgment.
• Careful prompt design and critical evaluation of LLM outputs are vital for maintaining high standards of patient care.