AI Limits
Why AI Sometimes Makes Things Up
A plain-English guide to AI hallucinations, made-up citations, invented details, and confident mistakes.
The common situation
The AI gives a confident answer that later turns out to be wrong. It may invent a source, mix up names, or create details that sound plausible.
Common reasons this happens
- AI models generate likely text patterns rather than guarantee verified facts.
- The prompt may pressure the model to answer even when evidence is weak.
- The topic may be obscure, recent, or poorly represented in training data.
- Long conversations can mix details from different contexts.
- Some tools do not verify citations unless they have live browsing or document access.
What you can try safely
- Ask for sources and verify them independently.
- Request a confidence note and assumptions.
- Use official sources for facts that matter.
- Ask the AI to say “unknown” when evidence is missing.
- Check names, dates, prices, laws, and technical details before publishing.
Interpretation rule: a product limit is not the same as a technical failure or a safety refusal. Identify the boundary before choosing the next step.
What this does not necessarily mean
A hallucination does not mean the AI is intentionally lying. It means the output can be fluent without being verified.
When official support is the right path
Provider feedback may help improve products, but you remain responsible for verifying important information.