One of the most common complaints about AI is some version of this: “It gives me generic answers. Polished, plausible, and useless.”
The complaint is accurate – and in most cases it describes the request more than the tool.
Why does AI give me generic answers?
AI is trained on roughly everything, so when you give it nothing specific, it returns the average of everything: correct-sounding, broadly applicable, and tailored to no one. A vague request gets the generic answer because the generic answer is genuinely the best response to a vague request. The output isn’t failing to be specific; your input was.
You can test this yourself. Ask AI to “write a post about leadership” and you’ll get the same interchangeable post everyone else gets. Now tell it who you are, who the post is for, the one point you want to land, an example from your own week, and what you want a reader to do afterwards. Different tool, suddenly.
AI multiplies what you bring it
Give two people the same AI and they will get very different results out of it. The tool is identical for both, so the difference is entirely in what each person brings to it.
Bring a sharp question and AI sharpens it further. Bring real context and the answer fits your situation instead of everyone’s. Bring a clear picture of the end product and you can see how far AI’s draft falls short. Bring standards, meaning the willingness to question what comes back, and confident nonsense gets caught before it goes out.
Bring none of that, and AI amplifies the vagueness instead: vague in, generic out, at incredible speed and volume.
This is why I keep coming back to one sentence: AI amplifies whatever clarity and rigour you bring to it.
How do I get from a good AI answer to a great one?
Give AI real context and the generic answer goes away. What comes back is good: accurate, relevant, usable. Good is usually where people stop.
Great work commits to something instead of covering the options. If a draft lays out the choices and leaves the choosing to you, it has left the hardest part undone; ask whether you’d defend its recommendation in a meeting. Great work could only have come from you: if it could have gone to a different client, or come from anybody else in your job, with barely a change, it’s still generic underneath. And great work has nothing in it filling space, so ask what you’d cut if you had to lose a third of it, and if that’s an easy question, cut it now.
Getting there depends on knowing what you’re aiming at in enough detail to judge what you got. A lot of people can’t do that, and it has little to do with effort. You can usually recognize good work in your field when you read it. Working out what makes it good is a separate skill, and there’s rarely a reason to practise it until you need to ask somebody, or something, to produce that standard for you.
So collect it. When you read a proposal, a memo, a report or a page of documentation in your own field that you’d have been proud to produce, keep a copy. A handful of those, read closely enough that you can say what makes each one work, teaches you your own standard better than any general description of good work, and it gives you something concrete to hand AI as the target.
Naming what’s wrong is a skill you can practise
When a draft is nearly right, the natural response is “make it better”, or “make it sharper”, and back comes a different draft with the same problem. AI works on what you tell it, so a vague complaint gets a vague repair.
Naming the problem is the skill, and it’s specific work: the second paragraph explains something the reader already knows; this gives four options when I need a recommendation; the tone belongs to a consultant and I want it to sound like somebody who has done the job; the argument rests on a claim I can’t verify. Every one of those is something AI can act on.
This is where a lot of people stall. School and work both train you to produce something and to judge whether it’s good enough to send. Taking work that’s already good enough and finding what would make it excellent is a different skill, and you can go a whole career without practising it deliberately. Rushing makes it worse, and slowing down on its own won’t fix it.
The next time AI writes something you’d normally accept, write down what’s wrong with it in specific sentences before you accept it, and send the original anyway if you like. That’s the judgment you’ll want ready on the work where it counts, and it gets quicker with repetition.
Clarity is being able to picture what you’re aiming at. Rigour is being able to say what’s missing from what you got. Both improve with deliberate practice – the part no tool hands you.
If you’d like to work on either of those with somebody, book a free intro call.