AI Without the Hype · Part 5 of 10
If AI saves ten minutes and you spend fifteen checking and correcting the result, the workflow needs work.
The answer is not to stop checking. It is to make the task easier to verify, give the reviewer a clear standard, and find out whether the result still saves effort.
That starts before the tool produces anything. Decide what a correct answer must contain and which errors would make it unacceptable.
Give the reviewer a source and a specific job
Imagine an office using AI to turn a supplier’s delivery notice into an internal update. The reviewer should have the original notice beside the draft. Their job is to confirm the order number, items, dates, exceptions, and next action.
“Looks good” is a weak review standard. “Matches the notice on these five points and adds no commitments” is much more useful.
NIST’s Generative AI Profile identifies confidently presented false content as a risk. Fluent wording does not establish that a statement is supported. Your review needs to reach the source information, rather than rely on how plausible the draft sounds.
Ask for an output you can inspect
For the delivery task, ask the tool to return a short set of fields: order reference, stated delivery date, affected items, unresolved questions, and a draft update. Tell it to mark missing information instead of filling it in.
If the source is long, ask it to identify the section supporting each important detail. Then check those references. A reference that looks precise still needs to point to something real.
This format puts the consequential details where the reviewer can see them. It also makes missing information more obvious than a polished paragraph that smoothly talks around a gap.
Do not assume an instruction guarantees compliance. Its value is that it defines behavior you can test.
Separate factual checks from editorial checks
First check whether the facts are right. Dates, quantities, names, prices, eligibility rules, and commitments deserve particular attention when they matter to the task.
Use the source system for authoritative records. Recalculate important arithmetic in a calculator or a checked spreadsheet. If a task requires a current policy, verify that the document used is the current approved version.
Then review tone and clarity. Is the message understandable? Does it sound appropriate for the recipient? Is anything unnecessarily long or dismissive?
A nicely written response with the wrong delivery date is still wrong. Keep the review in that order.
Test the cases that make the job difficult
An ordinary notice with one date tells you very little about how the workflow handles ambiguity. Include a revised delivery date, a partial shipment, a canceled order, and a message that does not provide the answer.
Keep a small collection of these examples with the expected outcome. Use approved or fictional data. Re-run them after changing the prompt, source material, model, or connection.
This is a practical check on your workflow. It does not prove the system will handle every future case. It gives you a repeatable way to notice when an improvement in one area breaks something else.
A second AI opinion can help, but it is not proof
You can ask another AI pass to look for omissions or contradictions. Treat those findings as leads for the reviewer.
Agreement between two outputs does not replace checking the delivery notice. Both may make the same assumption, overlook the same missing detail, or accept the same bad source.
The deciding evidence is still the source and the business rule the work must satisfy.
Record the errors that matter
For an initial trial, have a person review every result. Record material corrections separately from preferences about wording. “Changed the greeting” and “invented a promised delivery date” should not count as the same kind of problem.
Time the full task through approval, including retries. If the team repeatedly fixes the same mistake, change the workflow and test again. Better input structure or a narrower task may help more than another instruction telling the model to be careful.
Any later reduction in review should be a deliberate decision based on observed performance and the consequences of a mistake. There is no universal accuracy percentage that makes every use safe.
The result you are looking for
A useful workflow gives an informed person enough evidence to approve the work faster and with acceptable quality. If the reviewer must reconstruct the entire job every time, you have found a limitation worth addressing before expanding.
Keep that result visible. The goal is useful, approved work, with the checking included in the price.
Sources
NIST: Generative Artificial Intelligence Profile, especially its discussion of confabulation. NIST: AI measurement and evaluation. The review procedure above is a practical recommendation, not a NIST certification method.