Prompt engineering
The practice of writing and refining prompts so a model gives more reliable, useful answers. It covers giving clear instructions, supplying the right context, showing examples, and asking for a specific format. For a clinical or decision-making audience the point is not that this is a special skill, but that the quality of an AI tool in real use depends partly on how the prompts behind it are built, and that a vendor demo with carefully engineered prompts may not reflect what a busy user types at 2am.
A patient education team spends weeks refining the instructions behind a tool that drafts discharge leaflets, until it reliably writes at a sixth-grade reading level and always ends with when to call the clinic. Months later the provider updates the underlying model, and leaflets start coming back longer and without the call-the-clinic section, although no one changed the prompt. Keep the test cases the team used while tuning, and before accepting any new model version, rerun them and have someone read the output against the original requirements.
Terms like this come up in real clinical scenarios across the HelloAI courses: bite-sized modules with verifiable certificates. An account takes one minute, no password needed.
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