Prompt Engineering - The Anatomy of a Good Prompt: Why Most Clinicians Get Weak Results
Most clinicians who try AI tools for the first time and find them disappointing are not using bad tools. They are using good tools badly. And the gap between disappointing results and genuinely useful results almost always comes down to the quality of the instructions given to the AI.
Those instructions are called prompts. Prompt engineering is the practice of writing those instructions clearly enough that the AI can produce what you actually need. It sounds technical. It is not. It is closer to the skill of writing a clear consultation request than it is to programming. And like a good consult note, it requires you to be specific about what you want, why you want it, and what constraints apply.
What a prompt actually is
A prompt is simply everything you type or say to an AI before it responds. It can be one sentence or several paragraphs. It can include background context, specific instructions, examples of what good output looks like, and constraints about what to avoid. The more clearly and completely you communicate what you need, the better the AI can respond.
Most people start with prompts that look like search engine queries. A few words, a vague topic, an implicit expectation that the AI will figure out what they meant. Search engines have trained us to communicate this way. But AI assistants are not search engines. They respond to language the way a capable colleague would: they do their best with the information you give them, and if you give them very little, they make assumptions that may or may not match what you needed.
The most common mistake clinicians make
The single most common mistake is being vague about the task. A prompt like tell me about preeclampsia will produce a generic overview that tells you nothing you did not already know. It is the clinical equivalent of asking a medical student to talk about cardiovascular disease for a few minutes. You will get something, but it will not be what you needed.
