AI Glossary
Hallucination
When an AI model generates information that sounds plausible but is factually incorrect or fabricated. Hallucinations are a key risk in enterprise AI and can be mitigated with RAG and human oversight.
Understanding Hallucination
Hallucinations occur because language models generate text based on statistical patterns, not factual knowledge. The model produces what sounds right based on its training data, which sometimes means confidently stating things that are simply wrong.
For businesses, hallucinations are the primary risk in deploying AI for customer-facing or high-stakes applications. An AI that confidently provides incorrect contract terms, wrong product specifications, or fabricated statistics can cause real damage.
Mitigation strategies include RAG (grounding responses in your actual data), constrained outputs (limiting what the AI can say), confidence scoring, human-in-the-loop review for critical decisions, and automated fact-checking against authoritative sources.
Hallucination in Canada
Canadian businesses in regulated industries (finance, healthcare, legal) face heightened liability risks from AI hallucinations and should implement rigorous verification workflows.
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Frequently Asked Questions
Not entirely with current technology. However, RAG, constrained outputs, and human review can reduce hallucination rates to near zero for specific, well-defined tasks.
Cross-reference AI outputs against authoritative sources. Implement automated checks where possible, and always have human review for high-stakes decisions, legal content, or published materials.
See Hallucination in Action
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