AI Hallucination
AI hallucinations are confident but incorrect responses. Understanding them is essential for safe AI deployment.
When an AI model generates information that sounds plausible but is factually incorrect or entirely fabricated - a known limitation of language models that makes guardrails, source attribution, and human oversight essential.
What Hallucinations Look Like
The AI states a fact confidently - a date, a policy, a price - that sounds right but isn't. It doesn't know it's wrong.
Why They Happen
Language models generate probable text, not verified facts. When uncertain, they may produce plausible-sounding but incorrect content.
RAG as Mitigation
Retrieval-Augmented Generation grounds responses in your documents - dramatically reducing hallucination risk.
Approval Flows
Require human review for high-stakes responses. The agent drafts, you verify before the customer sees it.
Source Attribution
When the agent cites a source, users can verify. When it can't cite a source, it should say so.
Honest Uncertainty
Well-configured agents say "I don't know" or "let me check with the team" instead of guessing.
FAQ
Can hallucinations be completely eliminated?
No. But they can be reduced to near-zero for specific tasks using RAG, guardrails, and approval flows.
Are hallucinations dangerous?
In uncontrolled deployments, yes. With proper guardrails - approval flows, source attribution, escalation rules - the risk is manageable.
How do I know if my agent is hallucinating?
Source attribution helps. If the agent can't point to a document backing its claim, treat it with appropriate skepticism.
Deploy AI safely
Learn how guardrails prevent AI hallucinations.