Why does ChatGPT say wrong things about my product?
Usually one of three causes: stale training data (the model learned an old version of your pricing or features), outdated third-party pages it retrieves and trusts, or confident inference — the model filling a gap with whatever is plausible for products like yours. The fix is source repair plus verification, not arguing with the model.
Diagnose by asking where the claim could have come from. Wrong pricing usually traces to an old pricing page cached in training data or an outdated review-site listing. A 'missing' feature usually means your documentation never states it plainly enough to be extractable. A flatly invented capability usually means the model generalized from your competitors.
In search-enabled modes you can often see the source directly: the citation list tells you which page taught the engine the wrong fact. That page — yours or a third party's — is your repair target. In pure training-data answers there is no citation, but publishing a clear, current, well-structured correction gives both future retrieval and future training runs something better to learn.
Two practical notes. First, wrong answers are sticky: an engine that has one authoritative-looking stale source will keep using it until a better one exists. Second, the answer you get logged into your own ChatGPT account is contaminated by your history — verify with clean sessions, or use a monitoring tool that queries neutrally.
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Last updated: 2026-07-24