Discernment is the quality control. It is the habit of looking at what AI gives back and deciding whether it is actually good, not just whether it sounds good. A lot of teams get caught here, because fluent, confident writing is easy to mistake for correct writing.
What this means for your team
Discernment has three things to look at, and AI can be strong on one while quietly failing another:
- Is it right? Facts, figures, names, logic. The model will state a wrong detail as confidently as a right one, so anything that matters gets checked.
- Is it fit for purpose? Right tone, right length, right audience. Often technically fine but not what you needed.
- Is the reasoning sound? For anything analytical, follow how it got there, not just the conclusion. A good answer for a bad reason will not hold up.
Description and discernment work as a loop: you brief, you judge what comes back, you adjust the brief. Most of the skill is going round that loop quickly rather than accepting the first thing you get.
Try this
Next time someone shares an AI-assisted piece of work, ask one question: what did you check, and what did you take on trust? It is a quick way to see where discernment is strong and where it is missing.
Common questions about judging AI output
How do you know if you can trust AI's output?
You do not trust it on sight, you check it. AI states a wrong detail as confidently as a right one, so fluent, confident writing is easy to mistake for correct writing. The honest answer is that you can rely on the parts you have checked and the parts that do not matter much if they are wrong, and nothing else gets a free pass.
How do you judge whether AI output is good?
Judging output well means looking at three things, because AI can be strong on one and quietly failing another:
- Is it right? Facts, figures, names and logic. The model will state a wrong detail as confidently as a right one, so anything that matters gets checked.
- Is it fit for purpose? The right tone, length and audience. Output is often technically fine but not what you actually needed.
- Is the reasoning sound? For anything analytical, follow how it got there, not just the conclusion. A good answer reached for a bad reason will not hold up.
How do you spot when AI is subtly wrong?
The subtle errors hide behind confident, well structured writing, so you cannot rely on how it reads. Follow the reasoning rather than the conclusion: trace how it reached the answer, because a good answer reached for a bad reason will not hold up. Watch the process over several rounds too, since you can notice an idea you had already rejected quietly creeping back into the work.
What should you do when AI gives you the wrong thing?
Treat it as a loop rather than a verdict. Description and discernment work together: you brief, you judge what comes back, then you adjust the brief and go again, and most of the skill is going round that loop quickly instead of accepting the first thing you get. When you flag the problem, say what is wrong and why, not just that you dislike it, so the next version actually moves. If better briefing keeps failing, the problem may be further back, in what you handed to AI in the first place.