Treat fluent output as something to examine. Keep the source material and make checking part of the task.
Confidence is not a verification step
Generative AI can produce false or misleading information in an answer that sounds convincing. NIST describes this risk as confabulation in its Generative AI Profile. Clear writing alone is not evidence that the content is correct.
For a business owner, the important consequence is practical: a result needs a checking method suited to the task. Reading quickly and thinking “that sounds right” may not be enough.
Try a small example
Imagine asking a tool to draft an answer about your cancellation policy. The draft is friendly and well organized, but it says customers can cancel with 24 hours’ notice. Your policy says 48 hours. The tone is useful; the invented or altered detail is not.
The right review is to compare the answer with the actual policy. Ask the tool to work only from the supplied text and flag missing information, but still check the result yourself. Instructions help define the task; they do not replace review.
Build a short checking routine
- Names and numbers: compare them with the original records.
- Promises: check deadlines, prices, availability, and policy language.
- Sources: open any references and confirm they support the statement.
- Missing details: look for assumptions presented as facts.
- Final use: decide who can approve the result before it reaches a customer.
These are useful habits for an ordinary draft. Work involving legal, medical, financial, or other consequential decisions needs the appropriate qualified review. An attractive explanation does not make the tool the decision-maker.
Include the time spent reviewing
If an experiment produces a draft in seconds but takes longer to correct than your current process, that matters. Include review time and errors when you assess whether the tool is useful.
Keep a few examples of where it helped and where it missed the point. Those examples make a better discussion than a general claim that the tool is either brilliant or useless. The same tool may be useful for one task and unsuitable for another.
Our first experiment guide shows how to structure a small comparison. If you want help interpreting the results, we can work through them in a tailored walkthrough.