Correcting an AI mistake: what to do after the assistant gets it wrong

An assistant that never errs does not exist; what keeps trust is a correction that is fast, plain, and final.

Sooner or later it happens: the assistant gives a customer a wrong answer. A fact that is not a fact, a promise nobody authorized, a confident misreading of the sources it was given. What decides whether the relationship survives is not the mistake. It is the three jobs that follow it, and the order in which they get done.

The first job is the customer surface, and it belongs to a human. Own it fast, correct it plainly, and do not shift the blame onto the technology. The customer did not talk to software in their own mind; they talked to the company, and the correction should come from the company's voice. We hold that line on our own channels: our assistant says plainly that it is AI, and when something needs correcting, a person sends the correction.

The second job is the conversation record. The correction lands in the same thread where the wrong answer lives, so the story is whole when anyone reads it later: the error, then the correction, then the apology where one is due. A correction sent through a different channel, or a thread quietly edited, leaves two versions of the truth standing, and customers eventually find the version you did not approve.

The third job is the knowledge fix. Somewhere behind the wrong answer sits a source, a rule, or a missing line that produced it, and that is where the repair belongs: in the source, the same day, so the same answer cannot be given to the next customer. The failure version is the fix that happened in someone's head and nowhere else, while the same wrong answer went out again and again and one person nodded and said it would not happen again.

The failure versions teach the rest. The team that quietly fixed the source and never told the customer who was mis-told meets that customer again, and the conversation opens on a contradiction neither side can place. The correction that blamed a technical glitch is heard as something worse than the error itself: the company does not know its own business. And the log-reading rhythm, the habit of reading real conversations on a schedule, is what catches whether the error reached others before it was caught.

Guidance, not guarantees: we build assistants that say what they are, and we correct in our own voice when they fall short, because an assistant that never errs does not exist. What your corrections should look like, which sources your answers rest on, and how your team reads its own logs are questions with specific answers we are glad to work through with you; the principle stands above all of them, and it is the same every time: fast, plain, and final.