How to Spot-Check AI Output Every Week
You can start checking AI output with about thirty minutes a week. Pull ten real answers your team actually used, trace each one back to its source, and score it right, wrong but plausible, or unsupported. Tracked over time, the results give you a rough measure of how often the answers you sampled hold up, and that's a number many teams don't have.
Many teams skip this, for an understandable reason: checking everything defeats the point of using AI, so little gets checked. A sample offers a workable middle ground. A small, regular sample beats an occasional audit nobody has time for. It's the question from this week's Monday post, turned into a routine.
Why a sample is enough
You don't need to review every answer to start learning how reliable a workflow is. You need real answers, chosen without cherry-picking, and a consistent way to check them. Ten is a rough read, not a statistical result, and that's fine. The goal is to catch problems early and watch for recurring errors, not to produce a definitive accuracy rate.
It's the same logic behind the audit trails in a reconciliation engine. You don't need to check every row to start seeing where the exceptions cluster.
The weekly routine
- Pull ten. Choose answers the team relied on that week: a summary used in a meeting, a policy answer given to a customer, a figure that went into a report. Take them from the chat history or the log, not from memory, and don't pick the ones you already trust.
- Find the source. For each answer, identify the document, record, or page it should come from. If you can't find one, that is already a result.
- Compare. Open the source and read the answer against it. Does the source actually say that? Has the answer left out a qualification or introduced a claim the source doesn't support?
- Score it. Right means the source supports the answer. Wrong but plausible means the answer sounds reasonable but conflicts with the source. Unsupported means you can't substantiate the answer from the available evidence.
- Log it. One row per answer: date, workflow, score, and one line on what went wrong. Use the same sheet every week.
The first run is slow. By the third, it may fit inside thirty minutes, especially if the sources are easy to find.
Reading the score
Count by type, not just in total. The mix helps you decide what to investigate.
- Mostly wrong but plausible. Check for outdated or duplicated documents, conflicting versions, retrieval problems, or errors in how the AI interpreted the source. The fix depends on what you find.
- Mostly unsupported. Find out whether the information is missing, inaccessible, or present in the source material but overlooked by the AI. The answer may also be an invention with no basis in the available evidence.
- Mostly right, with an occasional miss. That's encouraging. Keep sampling and watch the trend rather than assuming the workflow will stay reliable.
One week tells you very little about overall reliability. Several months of consistent checks can reveal recurring problems or changes worth investigating. If results deteriorate after a model change, a content update, or a new team adopting the workflow, the log gives you a place to start.
Making it stick
Name one owner and keep the sheet in one place. Review it at the same meeting every month. Decide in advance what result triggers action, before you see a number you'd rather explain away.
The log pays off in a second place too. A record of how sampled AI answers performed sits naturally next to hours recovered and cycle time when the AI line item gets questioned. None of those measures tells the whole story, but together they give you a better basis for judging whether the workflow is working.
You don't know how accurate your AI is until someone checks, and the check only becomes useful when it's repeated.
Setting up a spot-check routine that fits how your team already works is a short working session. Get in touch if you'd like help building one.