Most disappointing AI output is not a model problem. It is a prompt problem, and usually the same four problems.
Give it a role, not a task
There is a real difference between write me a job description and you are a hiring manager who writes honest job posts. The first gets you the average of everything the model has read. The second narrows it to a particular kind of writing with particular standards. The role is the cheapest quality improvement available.
Say what good looks like
A model cannot infer your standards. If you want short, say how short. If you hate a phrase, ban it. Some of the most useful lines in a prompt are prohibitions: no exclamation marks, do not say we sincerely apologise, no bullet points.
Give it your material
The difference between a generic output and a useful one is almost always the raw material you paste in. Your real copy, your actual schema, the customer message as it was written. Summarising your input before you paste it throws away the specificity you are asking the model to work with.
Ask for the reasoning, not just the answer
Asking a model to state its assumptions, flag what it is unsure about, or name the weakest part of its own output costs you one extra line and gives you something you can check. An answer you cannot verify is worth less than a slightly worse answer you can.
A quick test
- Could I send this prompt to a competitor and get an equally useful answer? If yes, it is too generic.
- Have I told it what to avoid, not just what to do?
- Have I pasted real material, or described it?
- Can I check the output is right?
Fix those four and most of the gap between mediocre and useful output closes.
