"Prompt engineering" sounds like a technical speciality. In business work it is closer to briefing: stating what you want, supplying the material, and being explicit about the standards the result has to meet.
Four things separate a business prompt from a casual one
- Real material, pasted in. Describing your data is not the same as providing it. The model cannot infer your pricing model from the phrase "our pricing model".
- A worked example. One example of an output you were happy with does more than three paragraphs of instruction. Two examples are better still.
- Explicit constraints. What not to invent, what not to include, and permission to say "I don't know". Most factual errors in business output are gaps the model politely filled.
- A named audience. "For a non-technical leadership team" and "for the delivery team" produce genuinely different documents.
A reusable shape
You are drafting [document] for [audience]. Context: [paste the real material]. Task: [one outcome]. Format: [sections, length]. Constraints: use only the facts above; flag anything unstated under "Needs confirmation" rather than guessing; match the tone of this example: [paste two sentences of your own writing].
Treat prompts as assets
The difference between someone who is good at this and someone who is not is rarely vocabulary. It is that the first person kept the prompt. A shared place where your team stores the prompts that worked — with a line on what each one is for — compounds faster than any individual's skill.
Where it breaks
Prompts fail predictably: too many tasks at once, no example, no constraints, and reviewing output you have no way to check. If you cannot tell whether the answer is right, the task is not ready for automation yet.
Try this today
Take the last prompt you were disappointed by and add the two things it was missing: a real example of good output, and one sentence about what the model must not invent.