Hallucinations arise because language models generate the statistically likely next words, not verified facts; without strong grounding in retrieved source material, a model can fill gaps with plausible-sounding but invented details, including misattributed quotes, wrong dates, or product claims no source ever made. Retrieval reduces hallucination risk but does not eliminate it entirely.
For brands, hallucination cuts both ways: an engine might understate a real capability, overstate a limitation, or attribute a competitor’s feature to the wrong company, all without malice. Monitoring what answer engines actually say about a brand is the only way to catch these errors, since they occur silently and can persist across many user sessions before anyone notices.