Prompt design is crucial when using Large Language Models (LLMs) because it directly impacts the accuracy, relevance, and clarity of the generated responses. Well-structured prompts provide the necessary context and instructions, guiding the model to produce more reliable and useful outputs. Poorly designed prompts can lead to vague, biased, or even incorrect information, reducing the effectiveness of AI in critical applications like healthcare and education. By refining the text that you insert into an LLM, or “prompt design”, users can optimize LLM performance, ensuring better alignment with their specific needs and objectives.

Prompt development has two key aspects: prompt design and prompt engineering. Prompt engineering (PE) refers to the professional, iterative process of refining prompts, while prompt design focuses on creating tailored prompts for specific cases. Unlike search engines that rely on keywords for information retrieval, LLMs such as ChatGPT leverage Deep Learning and Natural Language Processing to interpret context, generating personalized and conversational responses, a phenomenon called contextual emergence ability. While LLMs create the appearance of understanding by processing prompts and generating language, they lack true comprehension. Instead, they analyze patterns within their training data to produce coherent responses, simulating understanding without possessing real comprehension, emotions, or consciousness.

A recent guide, A Guide to Prompt Design: Foundations and Applications for Healthcare Simulationists, provides a structured approach to leveraging Large Language Models (LLMs) like ChatGPT in simulation-based education (SBE). This guide not only highlights best practices for crafting effective prompts but also addresses ethical challenges and practical applications.


Best Practices for Effective Prompt Design

To optimize AI's role in healthcare simulation, the guide suggests:

  1. Clarity: Clearly define the expected output to minimize ambiguity.
  2. Context: Provide detailed background information to ensure relevant responses.
  3. Goal Alignment: Align prompts with specific educational objectives.
  4. Output Specification: Specify response formats (e.g., lists, reports, dialogues).
  5. Ethical Guardrails: Implement safety measures to prevent misinformation and ensure compliance.
    By adopting these strategies, educators can use AI as a powerful tool to enhance training while mitigating risks.


Use Cases

Recent advancements have demonstrated the significant potential of LLMs in role-playing as patients for healthcare students. AI chatbots are now commonly used as “virtual patients” integrated with other platforms and commercial products (1, 2). LLMs have been used to develop virtual patients that mirror real-life counterparts, enabling learners to practice communication through voice recognition instead of a text-based interface (3).


LLMs have also demonstrated significant potential in improving communication and information processing in healthcare training (2, 4, 5). They can automatically transcribe spoken feedback during simulations, giving trainees a written record to review and reflect upon (4). Additionally, LLMs can extract and summarize key insights from large volumes of feedback, helping trainees prioritize learning objectives and focus on critical areas for improvement (4).


A Guide to Prompt Design: Foundations and Applications for Healthcare Simulationists further discusses use cases and limitations of LLMs in SBE. As AI evolves, ongoing research and professional development will be key to integrating these technologies responsibly in healthcare education.

Sources


1. Holderried, F, Stegemann-Philipps, C, Herschbach, L, Moldt, J, Nevins, A, Griewatz, J, et al. A generative Pretrained transformer (GPT)–powered Chatbot as a simulated patient to practice history taking: prospective. Mixed Methods Study JMIR Med Educ. (2024) 10:e53961. doi: 10.2196/53961

2. Sardesai, N, Russo, P, Martin, J, and Sardesai, A. Utilizing generative conversational artificial intelligence to create simulated patient encounters: a pilot study for anaesthesia training. Postgrad Med J. (2024) 100:237–41. doi: 10.1093/postmj/qgad137

3. Borg, A, Parodis, I, and Skantze, G. Creating virtual patients using robots and large language models: a preliminary study with medical students. In: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (2024), p. 273–277. Available at: https://dl.acm.org/doi/pdf/10.1145/3610978.3640592 (Accessed June 24, 2024).

4. Varas Cohen, JE, Coronel, BV, Villagrán, I, Escalona, G, Hernandez, R, Schuit, G, et al. Innovations in surgical training: exploring the role of artificial intelligence and large language models (LLM). Rev Col Bras Cir. (2023) 50:e20233605. doi: 10.1590/0100-6991e-20233605-en

5. Benfatah, M, Youlyouz-Marfak, I, Saad, E, Hilali, A, Nejjari, C, and Marfak, A. Impact of artificial intelligence-enhanced debriefing on clinical skills development in nursing students: a comparative study. Teach Learn Nurs. (2024) 19:e574–9. doi: 10.1016/j.teln.2024.04.007