The four causes of wrong AI answers
Cause one: stale training data. The model learned about your business as it existed before its knowledge cutoff. If you rebranded, repriced or relaunched since, the model remembers the old version until retrieval overrides it.
Cause two: weak retrieval. When an assistant searches the live web and your own pages do not state the fact plainly, it settles for whatever it finds. Vague marketing copy loses to a competitor comparison page that mentions you specifically, even when that page is wrong.
Cause three: outdated third party sources. Directories, review sites and old articles outrank your corrections in the sources assistants trust. The assistant is accurately quoting an inaccurate source.
Cause four: thin brand facts. If little has been written about you, assistants generalize from your category. Small brands get described by the average of their industry, not by their actual differences.
How to diagnose which cause you have
Ask the four major assistants the same questions your customers ask, and watch the citations. If ChatGPT cites a source, the error is retrievable and fixable at that source. If there are no citations and the answer sounds dated, you are looking at training data, and the fix is publishing strong, current facts for retrieval to find. If every assistant tells a slightly different wrong story, you have a scattered source problem across the third party layer.
This diagnosis is the first thing an AI visibility audit produces: your buyers’ real questions, the answers from ChatGPT, Gemini, Claude and Perplexity with screenshots, and the specific sources feeding each error.
Fixing it, and keeping it fixed
The repair sequence is covered step by step in how to correct wrong information in ChatGPT: state facts plainly on your own site, correct the third party sources assistants cite, publish fresh content for retrieval, and re-test monthly. The keeping-it-fixed part is where most teams fail, because AI answers shift as sources and models update. That is why Aethon pairs monitoring with an Action Engine that maps every wrong or missing answer to a concrete fix, and tracks whether the answer actually changed.
