Artificial intelligence holds transformative potential for healthcare, particularly when applied through large language models (LLMs). These models promise enhanced administrative efficiency, better clinical decision-making and improved patient experiences. However, the value of these tools depends heavily on how well users can guide them—a process known as prompt engineering. In healthcare, where accuracy, context and precision are non-negotiable, crafting effective prompts is not just a technical necessity but a strategic imperative.
Defining Prompt Engineering in the Medical Context
Prompt engineering refers to the careful design of instructions given to AI models, telling them what to do and how to do it. The goal is to ensure the AI interprets the task correctly and returns relevant, accurate and actionable outputs. In the healthcare industry, this means using natural language prompts that are clear, concise and well-structured, with expectations around response format and source material explicitly defined. For instance, a clinician might instruct the AI to provide a structured summary or generate a treatment plan using only peer-reviewed medical literature.
This process requires a deep understanding of both AI capabilities and healthcare nuances. Organisations must be diligent in specifying source preferences, output structure and the context of the task. Prompt engineering is often described as both an art and a science because it blends creativity with analytical thinking. The stakes are high in healthcare; incorrect or vague instructions could lead to irrelevant or misleading responses. As such, prompt engineering becomes not just a support function but a central mechanism in realising the value of AI investments.
Best Practices for Prompting LLMs in Healthcare
Successful prompt engineering hinges on several best practices. First, specificity is key. Vague prompts lead to ambiguous outputs, while highly detailed prompts ensure the AI remains focused on the task. Including the patient’s condition, comorbidities and treatment goals within the prompt can lead to more precise and relevant recommendations. Providing examples of the desired output—both good and bad—also helps the LLM understand the expectations, reducing the likelihood of surprises or inconsistencies.
Secondly, prompt engineering should be viewed as an iterative process. Initial responses often need refinement, and follow-up prompts can help add missing context or clarify complex conditions. This dialogue-like interaction helps improve the accuracy and usefulness of the AI’s outputs. Including prompts that ask the LLM to suggest better questions or seek clarification can lead to more insightful results. Over time, as healthcare providers interact more with AI tools, patterns in prompt effectiveness emerge, helping to streamline future interactions.
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Finally, user feedback is indispensable. Continuous testing and adaptation are necessary, particularly in clinical environments. Healthcare professionals and researchers should evaluate AI outputs, flag inconsistencies and recommend improvements. This feedback loop is essential not only for refining individual prompts but also for improving overall prompt strategies. As AI becomes more embedded in healthcare workflows, developing a culture of active engagement with these tools will be critical.
Customising Prompts for Different AI Models
Not all LLMs are created equal. Some models are designed for general use, while others are fine-tuned for specific fields, such as medicine. Consequently, effective prompt engineering must take into account the unique behaviour of each model. Generalist models may require more structured and explicit instructions, while clinical LLMs like Med-PaLM or BioGPT may respond well to more nuanced or implicit guidance due to their specialised training.
Users should experiment with different phrasing styles—direct commands versus conversational tone—to see what yields the best results. Some models may perform better when information is presented in bullet points or predefined templates. Others may thrive on narrative-style inputs. Understanding the nuances of each model’s training data, inference style and response tendencies is crucial to maximising performance.
Organisations should tailor their prompt strategies not just to the AI’s capabilities but also to the desired application. A model used for medical literature summarisation will need different prompts than one used for drafting patient communication or predicting treatment outcomes. Knowing the strengths and limitations of each model allows prompt engineers to create more effective and reliable workflows that align with clinical goals.
Prompt engineering plays a vital role in unlocking the full potential of AI in healthcare. As large language models become more sophisticated, the ability to guide these tools with precision will determine how well they serve clinicians, researchers and administrators. Best practices such as clarity, context, example-driven design and iterative refinement ensure LLMs deliver accurate, actionable results tailored to the complexities of medicine. Furthermore, adapting strategies to match the specific model in use strengthens the reliability and consistency of AI outputs. Moving forward, continued collaboration between healthcare professionals and AI specialists will be key to advancing prompt engineering as a cornerstone of digital healthcare transformation.
Source: HealthTech
Image Credit: iStock
