When AI answers are too vague, it is usually not because the model cannot answer, but because the task has not specified the audience, scope, and output format. Change these three things first, and the answer can usually be used right away.
Many people's first reaction is to add a long chunk of background, but that is the wrong order. No matter how much background you add, if you do not make clear who it is for, what standard to judge it by, and what form the final result should take, it can still only give you a generic draft that could work for anyone.
Why answers become vague
The root of vagueness is that the question is too open. If you only say write me a plan or give me some suggestions, it does not know who the reader is, which step needs to be solved, or how long it should be, so it can only list all the common points. It looks comprehensive, but it cannot be carried out in your specific situation.
Fix one: give the audience and the scenario
First, make clear who it is for and in what scenario it will be used. Even for the same notice, writing for newcomers attending an event for the first time and writing for veteran members who already know the process require completely different tones and details to highlight. Put the audience, scenario, and purpose at the start of your question, and it will stop circling around in a generic tone.
Fix two: give criteria or an example
Next, tell it what counts as good. You can give an example you approve of and ask it to follow that structure and tone; if you have no example, list the criteria directly, such as requiring specific times, being actionable step by step, and avoiding empty talk. With a reference point, it knows to write in detail rather than trying to cover everything.
Fix three: limit the output format
Finally, limit how it should deliver the result: how many words, how many items, whether to use a table, and whether you want only the conclusion or reasons as well. If you want only the conclusion, say clearly that you do not need an explanation of the process; if you need a comparison, ask for a table listing pros and cons. Once the format is limited, unnecessary padding naturally decreases.
When you follow up, you can say it directly like this: “Please rewrite it to the standard just mentioned: write it for a beginner encountering this for the first time, divide it into three items, give the action first and then the cautions for each item, keep the total length within three hundred words, and do not add a background introduction.” Stating the audience, criteria, and format all at once is far more effective than repeatedly saying be more specific.
When to stop following up
You also need to recognize the limits. Sometimes vagueness is not a problem with how you asked, but because the model truly does not know your internal details, or the material at hand is insufficient. In that case, trying ten other ways of asking will not save it. What you should do is feed it the documents, data, and drafts directly, and let it answer based on the material.
There is another situation where you should stop in time: if you have already followed up on the same question for three rounds and the answer is still empty and still repeating general principles, do not keep wrestling with the original question. Break the large task into smaller steps and let it solve one small problem at a time, which is often faster than continuing to follow up.
Next time you run into an answer that is too vague, do not rush to add background first. Fill in these three items — audience, criteria, and format — one by one and send it again. That is the next step you can take right away.
