Last month I was reviewing a case from one of our ongoing projects in Bangladesh. A junior doctor in a rural upazila health complex typed a simple prompt into a general purpose LLM: "Summarise this patient's history and suggest next steps for suspected dengue." The output looked clean. It listed symptoms correctly. It even cited a plausible WHO guideline. But buried in the response was a quiet hallucination: the model recommended a drug the patient was allergic to, based on nothing in the chart. The doctor caught it in time. Many would not.

This is not a rare story. It is the everyday reality when we move large language models from demos to deployment in real clinics.

Everywhere I look in the literature right now, experts are publishing careful tutorials and comparative studies on prompt engineering. They show how chain of thought, few shot examples, or meta cognitive prompts can lift accuracy by 10 to 15 percent. That work matters. But after reading the latest papers, I keep coming back to the same uncomfortable truth: prompt engineering is a useful starting point, not a safety solution.

The Illusion of Control

Prompt engineering gives a sense of precision. You specify tone, structure, constraints, even reasoning steps. In controlled settings, this works surprisingly well. Tutorials for clinicians show improvements in clarity, relevance, and even reduced hallucination rates.

Yet these improvements are fragile.

Change the input slightly. Introduce ambiguity. Add real world noise. The same carefully crafted prompt can fail quietly. The model still produces confident outputs, but the guarantees are gone. This is not a bug in prompting. It is a property of how these models work. Prompting does not change the underlying uncertainty. It only reshapes how that uncertainty is expressed.

What the Recent Research Actually Says

Workum and colleagues (Frontiers in AI, 2025):

They lay out a clear seven step pathway for safe LLM use in healthcare: protect privacy, adapt models with domain knowledge, tune hyperparameters, engineer prompts, separate clinical decision support from non decision support uses, evaluate outputs systematically, and put real governance in place. Notice where prompt engineering sits. It is step four. The authors treat it as necessary but never pretend it is sufficient.
Comparative analysis in BMC Medical Informatics and Decision Making:

Researchers tested three frontier models across six different prompting strategies on genuine clinical scenarios. The best strategy improved ethical reasoning and cut safety incidents by almost half. Yet even then, critical safety problems still appeared in more than 11 percent of responses.
From My Research:

In our under-review work on fairness-aware representation learning for biosignals and hybrid explainable systems for maternal health risk, pure prompt tricks improve surface performance but leave deeper failure modes untouched.

What Actually Works

Practical Lessons for Researchers and Clinicians

  • Treat prompt engineering as the entry ticket, not the destination.
  • Build in domain adaptation using retrieval-augmented generation with verified local guidelines.
  • Separate use cases: clinical decision support needs formal validation.
  • Implement structured evaluation frameworks like ACUTE.
  • Put governance in place before deployment.
  • Design for contestability.

A Final Thought

I keep thinking about the doctor in that rural upazila who caught the allergy mistake. She succeeded not because the prompt was perfect, but because she never fully trusted the machine in the first place. Our job as researchers is to build systems that earn that careful trust instead of demanding it.

References

Workum, J. D., et al. (2025). Bridging the gap: a practical step-by-step approach to warrant safe implementation of large language models in healthcare. Frontiers in Artificial Intelligence.
Esmaeilzadeh, P. (2025). Ethical implications of using general purpose LLMs in clinical settings. BMC Medical Informatics and Decision Making.