The most common experience with AI is that people tried it and it wrote generic things. That is nearly always true, and nearly always caused by the input.
A model does not know what it does not know. Give it no context and it produces the most general answer available, because that carries the least risk. A generic answer is not a fault. It is a mirror of the missing information.
Who it is for. Not "business owners", but "a managing director who already advertises and does not trust the report". The second one decides word choice, length and examples.
What decision you want. What the reader should do after reading. Without that, the text describes something but takes nobody anywhere.
What the input data is. If you have your own material, put it in. One real example is worth ten sentences of instruction. Whatever you do not provide, the model will invent.
What not to write. This is the most frequently skipped element and the most useful one. Forbidden claims, forbidden phrases, forbidden formats. This is what holds the voice.
What shape you want. Length, structure, how many points. Without it the model uses its own default, which is the same default everyone else gets.
The negative instruction. Say only what you want and the output drifts towards the average. Say what you will not accept and the output starts to resemble you.
Checking anything that is a factual claim. A model will write a convincing sentence around a number that does not exist. If a figure, a date, a regulation or a product specification appears in the output, it has to go back to the source. That is not distrust. It is the same loop you would run with a junior colleague.
A good brief is not longer. It is more specific.
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