There is a version of AI output that looks exactly like good work. It is fluent, structured, confident, and wrong in ways that are difficult to spot unless you already know the answer.
This is a reasoning problem. Build the skill to think before it concludes.
Make the process visible
An answer fills a gap. A conclusion is reached by a process. For diagnostic work, ask the model to work through each possible failure type, eliminate the ones that do not fit, and explain why before it classifies the case.
When there are multiple outcomes, require elimination before selection. Ask the skill to construct the strongest argument against its own conclusion. If the conclusion survives, it is more robust. If it does not, you caught the error before it shipped.
No conclusion without evidence. Require a direct quote from the input and calibrate confidence as High, Medium, or Low with a mandatory justification. High confidence without a quote is not confidence. It is theatre.
Absence is data too. What should be in the log that is not? A report discussing fourteen of fifteen items can look complete. Nothing in the prose will tell you what is missing unless you ask.
You are not writing a prompt. You are encoding how a rigorous analyst thinks, made consistent, made fast, and made auditable. The reasoning is the product, not the output.
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