Introduction
For decades, core clinical skills have included history-taking, examination, and interpreting investigations. In 2025, a new skill is rapidly joining that list: clinical prompt engineering. This is the art and science of asking an artificial intelligence the right question, in the right way, to get the most accurate, relevant, and safe output possible.
As AI tools, from general models like ChatGPT-5 to specialised clinical assistants like iatroX, become more integrated into healthcare, the quality of their output is often a direct reflection of the quality of the clinician's input. This article introduces the concept of prompt engineering as a new professional competency, provides a practical guide on how to structure your clinical queries, and argues that mastering this skill will make you a more efficient and effective clinician.
Why prompt engineering is now a core clinical skill
In the past, finding information involved knowing which database to search and which keywords to use. With modern AI, the interaction is conversational, but the underlying principle is the same: the better the query, the better the result. A poorly structured prompt can lead to vague, irrelevant, or even dangerously incorrect answers. A well-structured prompt, however, can yield a concise, evidence-based summary in seconds.
For the AI-augmented doctor, mastering this skill is not just about efficiency; it's about safety and professionalism. It allows you to harness the power of these incredible tools while maintaining full clinical oversight.
A practical guide: how to structure a clinical query
Effective clinical prompting goes beyond a simple question. It involves providing context, defining the role of the AI, and specifying the desired output.
The basic formula: Role + Task + Context + Format (RTCF)
A powerful way to structure any clinical prompt is to include these four elements:
- Role: Tell the AI who it should be. This focuses its knowledge base.
- Task: Tell the AI exactly what you want it to do.
- Context: Provide the specific, de-identified clinical information.
- Format: Tell the AI how you want the answer presented.
Examples: ineffective vs. effective prompts
Let's take a common clinical scenario and see how we can improve the prompt.
Scenario: A 65-year-old man with type 2 diabetes and stage 3 chronic kidney disease (CKD) presents with a new diagnosis of atrial fibrillation (AF). You need to consider anticoagulation.
Ineffective Prompt (too simple):
"anticoagulation in AF and CKD"
- Problem: This is a keyword search, not a prompt. It's vague, lacks context, and doesn't specify the desired output. The AI might return a general essay, a list of drugs, or non-UK specific guidance.
Better Prompt (using RTCF for a general AI like ChatGPT-5):
Role: "You are a UK-based clinical pharmacist." Task: "Summarise the key considerations for starting anticoagulation for a patient with new-onset atrial fibrillation, specifically addressing the challenges of moderate renal impairment." Context: "The patient has an eGFR of 45 ml/min. The CHA₂DS₂-VASc score is 3." Format: "Provide the answer as a series of bullet points, referencing the relevant NICE guideline where possible."
- Why it's better: This prompt is specific, provides all the necessary clinical context, and defines the format, leading to a much more useful and safer answer.
Best Prompt (for a specialised, UK-guideline tool like iatroX):
"What are the recommended DOAC dosing adjustments for a patient with an eGFR of 45 and new atrial fibrillation, as per the BNF and NICE NG196?"
- Why it's best for the tool: Specialised tools like iatroX are already pre-configured with the "Role" (a UK clinical expert) and are designed to search a specific "Context" (UK guidelines). Therefore, the most effective prompt is a direct, precise question that includes the specific guideline or source you want it to reference.
The professional development case
The ability to efficiently and safely retrieve high-quality information has always been a hallmark of a good clinician. Prompt engineering is simply the modern evolution of that skill. By investing a small amount of time in learning how to structure your queries, you can:
- Save significant time: Get the right answer the first time, every time.
- Improve the quality of your learning: By forcing you to structure your question clearly, prompt engineering deepens your own understanding of the clinical problem.
- Enhance patient safety: By getting more accurate and relevant outputs from AI tools, you reduce the risk of acting on flawed or out-of-context information.
This is a skill that will become increasingly valuable over the course of your career. It is a key area for personal and professional development that will pay dividends in efficiency, confidence, and safety.
Conclusion
As AI becomes more embedded in our clinical workflows, our ability to interact with it effectively will be a key differentiator. The "clinical prompt engineer" is not a new job title, but a new set of skills for every modern clinician. By moving beyond simple keyword searches and adopting a structured, thoughtful approach to how we ask questions, we can transform AI from a source of potential risk into our most powerful co-pilot, augmenting our own expertise to deliver better, safer care.