What is an AI hallucination?
An AI hallucination is when a language model produces information that sounds confident and plausible but is actually false or fabricated, because it generates likely-sounding text rather than retrieving verified facts. In data analysis this is dangerous: a chatbot might invent a number that was never in your data.
Hallucination is the core risk of using general-purpose AI for facts. Because language models predict plausible text, they can state wrong figures, cite non-existent sources, or confidently 'answer' a data question with a number they simply made up. For business decisions, that is unacceptable.
Why it happens:
- The model generates rather than looks up.
- It has no inherent access to your actual data.
- Fluent phrasing makes false answers seem trustworthy.
iDBQuery is architected specifically to avoid this. Rather than letting a model guess a number, iDBQuery translates your question into SQL that runs against your live data, so the figure returned is computed from your real records, not invented. Every answer is then cited back to the exact source rows, so you can click through and verify it. If iDBQuery cannot answer reliably, it is designed to say so or ask for clarification rather than fabricate. This grounding, real query plus row-level citations, is the difference between a plausible-sounding chatbot and an answer engine you can actually trust for decisions.
Updated 2026-06-22