When the answer about you is simply wrong

Sooner or later an assistant will say something false about you. A feature you never shipped, a price that was never real, a founder who does not exist, a confident sentence assembled from thin air. It is unsettling to read, and the instinct is to treat it as a glitch you cannot touch. Often you can do more than you think.

Most of these errors are not random. They grow in the gaps where reliable information is missing or contradictory. The model has to say something, finds too little to go on, and fills the space with a plausible guess. So the fix usually starts by closing the gap: publish the correct fact clearly, in a place that is easy to find, and make sure your other sources agree with it.

When the system relies on live retrieval, this works surprisingly fast. Give it a clean, current page that states the truth plainly, and the next time it searches, it has something better than its own invention to lean on. When the error is baked into the model's memory, it is slower, but a consistent and well-corroborated public record still pushes the retrieved answer toward reality.

You will not win every case, and some errors persist longer than they should. But treating hallucinations as fixable, by removing the ambiguity that bred them, beats treating them as weather you simply endure.