## Key Takeaways

- **A correction is an investment, not a chore.** Redoing the work yourself fixes one output. Correcting the AI employee properly fixes every future run of that task.
- **Most "AI mistakes" have one of four causes:** an ambiguous instruction, missing context, a stale tool connection, or a genuine model error. Each has a different fix, so diagnose before you correct.
- **Point at the exact error, not the general vibe.** "The revenue figure in row 12 is from March, we asked for April" gets a real fix. "This looks off" gets a rewrite that may be wrong in a new way.
- **Ask the AI employee to show its source.** A good AI employee can tell you where a number came from. If it cannot, that itself is the finding.
- **Every correction should end with a written rule.** If the fix only lives in one chat thread, the same mistake is waiting in next month's report.

![Four-step correction loop: name the error, read the explanation, fix the root cause, write the rule down](/images/blog/how-to-correct-your-ai-employee-when-it-gets-something-wrong/correction-loop.webp)

## The wrong number in an otherwise good report

Your AI employee posts the weekly sales summary in Slack. Nine numbers are right. One is not: the pipeline total includes two deals that closed lost on Tuesday. You have thirty seconds of annoyance and a choice to make. Fix the number yourself and move on, or correct the AI employee so next Friday's report is right on its own.

Most people quietly fix it themselves. That feels faster, and for this one report it is. But it also guarantees you will be fixing the same class of error every week, which is how teams end up saying "the AI is unreliable" about a mistake they never actually corrected. This post is the correction loop that makes errors stay fixed.

## Why did your AI employee get it wrong?

Before correcting anything, figure out which of four failure types you are looking at. The fix is different for each.

| Failure type | What it looks like | The fix |
| --- | --- | --- |
| Ambiguous instruction | You said "pipeline", it counted all open deals including stale ones | Rewrite the instruction with the exact definition |
| Missing context | It did not know deals marked "on hold" should be excluded | Give it the rule and have it saved for next time |
| Stale tool connection | The CRM data it pulled was hours old or the connection had expired | Reconnect the integration, then rerun |
| Genuine model error | It had the right data and instruction and still miscounted | Flag it, have it redo the step, add a self-check to the task |

The first two cause the large majority of real-world errors, and both are correctable in a way that sticks. The [guide to writing tasks your AI employee can execute](https://viktor.com/blog/how-to-write-tasks-for-your-ai-employee) covers prevention. This post covers what to do after the fact.

## The four-step correction loop

### Step 1: Name the exact error

Reply in the same thread as the wrong output, and be surgical about what is wrong:

```prompt
The pipeline total in this report is wrong. It shows $84K but two deals
(Northwind and Corella, both marked closed-lost on Tuesday) should not
be counted. Correct total should only include deals in stages
"qualified" through "contract sent". What source did you use for this number?
```

Two things are happening here. You are pinning the error to specific records, and you are asking for the source. Vague feedback like "the numbers seem off" forces the AI employee to guess at what displeased you, and a guess can introduce new errors while removing none.

### Step 2: Read the explanation before accepting the fix

A capable AI employee will answer with where the number came from: which tool it queried, what filter it applied, what it counted. Read that answer. It tells you which of the four failure types you have.

- "I counted every deal with an open date in July" means your instruction was ambiguous about stages.
- "The CRM query returned these 14 deals" with two closed-lost ones in the list means the data or filter is the problem.
- "I do not have access to the deal-stage field" means a connection or permission issue, not a reasoning issue.

This is also the moment to notice a deeper problem. If the explanation does not reconstruct where the number came from, treat the whole report as unverified. The [verification guide](https://viktor.com/blog/how-to-verify-your-ai-employees-work) goes deep on making every number traceable to a source.

### Step 3: Fix the root cause, not the symptom

Match the fix to the failure type. If the instruction was ambiguous, rewrite the recurring task definition with the exact filter, in words a new hire would understand: "Pipeline total = deals in stages qualified, proposal, contract sent. Exclude on-hold and closed-lost, always." If context was missing, state the rule once and say it applies from now on. If the connection was stale, reconnect it and rerun the task the same day, so you confirm the fix while the details are fresh.

Then have the AI employee redo the output. Do not patch the number by hand. The redo is your test that the correction actually worked.

### Step 4: Write the rule down

The correction is only finished when it outlives the thread. Ask the AI employee to record the rule in its standing instructions for that task:

```prompt
Save this as a permanent rule for the weekly sales summary: pipeline
total includes only deals in stages qualified, proposal, and contract
sent. Exclude anything marked on-hold or closed-lost. Confirm you saved it
and repeat the rule back to me.
```

The confirmation step matters. "Repeat the rule back to me" catches misunderstandings while they are still cheap. An AI employee with [persistent memory](https://viktor.com/blog/how-to-give-your-ai-employee-memory) will apply the rule on every future run, which is the whole point of correcting instead of redoing.

![A team escalating an urgent production question to their AI employee in Microsoft Teams](/images/blog/how-to-correct-your-ai-employee-when-it-gets-something-wrong/in-practice.webp)

## Correct it or redo it yourself?

There is an honest case for just fixing the output yourself: a one-off task you will never run again. For everything else, the math favors correction.

| Situation | Redo yourself | Correct the AI employee |
| --- | --- | --- |
| One-off task, never repeats | Reasonable | Overkill |
| Weekly or monthly recurring task | Fixes one instance of a permanent problem | Fixes all future runs |
| Error a client or exec already saw | Fix fast yourself, then correct the root cause after | Required, after the fast fix |
| Third occurrence of the same error | You are now the automation | Overdue |

The third-occurrence row is the one to watch. If you have fixed the same mistake three times by hand, the recurring cost is yours, and it was avoidable after occurrence one.

## When the same error keeps coming back

If you corrected properly and the mistake returns anyway, something structural is wrong. Check three things in order:

1. The rule was saved to the task, not just said in a thread. Ask the AI employee to show you the current standing instructions for the task and read them.
2. Two people are giving conflicting instructions. If a teammate told it something different last week, the task definition is now contradictory, and it needs one owner.
3. The task is genuinely beyond reliable automation in its current shape. Some tasks need restructuring into smaller steps before they run cleanly. The [guide to unsticking a stuck AI employee](https://viktor.com/blog/what-to-do-when-your-ai-employee-gets-stuck) covers how to break work down when a task keeps failing whole.

A useful habit for high-stakes recurring tasks: add a self-check line to the task itself, such as "before posting, state the source and record count for every figure in the report". It turns silent errors into visible ones.

## Frequently Asked Questions

### How do I give feedback to an AI employee effectively?

Reply in the same thread as the output, name the exact error with specifics (which figure, which row, what it should have been), and ask what source it used. Then fix the root cause and have the rule saved to the task so it applies to every future run.

### Why does my AI employee keep making the same mistake?

Usually because the correction lived in one conversation instead of the task's standing instructions. Ask it to show the current rules for the task. If your rule is missing, save it explicitly and have the AI employee repeat it back to confirm.

### Should I redo the work myself or correct the AI employee?

For a genuine one-off, redo it yourself. For anything recurring, correct the AI employee: naming the error and saving the rule takes a few minutes once, while hand-fixing costs you the same minutes every single run.

### Can an AI employee tell me where a number came from?

A well-built one can. Viktor works with live tool access, so he can state which integration he queried, what filter he applied, and which records he counted. If an AI tool cannot reconstruct its own sources, treat its numbers as unverified.

### What if the error already went to a client?

Fix the artifact fast yourself and send the correction, then run the correction loop afterward. Speed matters for the client; the root-cause fix matters for every report after this one. Doing only the fast fix is how the same error reaches the same client twice.

### Do corrections carry over to new tasks?

Rules saved to a specific task apply to that task. Broader preferences, like "always use ISO dates" or "exclude on-hold deals in every report", can be saved as general standing instructions so they apply across tasks. Say which scope you want when you save the rule.

---

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