Your AI workflow is only as good as your definition of good

AI can give you something that looks finished before you've decided what finished means.

Content design starts before the final wording: understanding a need, deciding what information matters and making the next action clear. With AI, those same habits apply to the workflow itself:

- Define the task and the criteria. "Write a customer email" asks for text. "Help this customer understand what's missing and what to do next" gives you something you can evaluate.
- Separate evidence from assumptions. "Requested", "approved" and "complete" shouldn't become interchangeable states.
- Design for exceptions. When a source is missing or two records disagree, the useful output is often a question, not a confident answer.
- Keep the judgment outside the model. Another model can help check, but the rules and the decision need to sit outside it.

A fictional example. AI drafts an installation update. The customer's preferred date is awaiting confirmation, but the draft says: "Your installation is booked for Tuesday." The wording is clear. The information is wrong. The fix isn't better wording - it's a rule: don't turn a requested date into a confirmed booking.

How do you define "good" before AI output ships on your team?