Most people accept an AI answer that is close enough or write a new prompt and hope for a better result. Neither teaches the AI anything.
The better option is to share the corrected version. Explain what changed and why. Give the AI the difference between what it produced and what you actually wanted. This is calibration, not complaint.
Why correction works better than redirection
A new prompt starts again. The useful details from the previous exchange are lost. A correction adds to a working model of your preferences: what was almost right, where the AI went wrong, and which assumption you did not share.
The difference compounds. A tool you correct gets progressively more aligned to your actual work. A tool you simply rerun gets no better.
What a useful correction looks like
Not “this isn’t quite right.” Say what changed and why: “I shortened the opening because the first two sentences repeated the same point. I changed ‘utilise’ to ‘use’ because I prefer plain English. I moved the example earlier because the argument needs grounding before the claim.”
The principle behind the change is what makes it reusable. If you make the same correction more than twice, codify it in the skill, project instructions, or saved preferences.
Matter-of-fact feedback works best. You are calibrating a tool, not marking homework. Every correction is a lesson. The ones you explain become permanent.
This is part of the Parenting Your AI series, a practitioner's guide to building AI skills that are safe, effective, and worth trusting. Written from inside enterprise AI systems by someone who has spent years diagnosing what goes wrong when AI meets real work at scale.
Read the full series at KnowledgeManagement.ie