What is AI Hallucination ?
An AI hallucination is when a language model generates information that sounds plausible but is factually incorrect, fabricated, or inconsistent with reality — often delivered with high confidence.
Large language models don't "know" facts the way databases do. They predict the next word based on patterns learned from training data. When the right answer isn't clearly supported by what the model has seen, it will sometimes confidently generate something that looks correct but isn't. This is hallucination — and it's the single biggest reliability issue in production AI deployments.
Common causes: the model wasn't trained on the relevant information, the question is ambiguous, the model has seen contradictory examples, or the model is too small to handle the complexity of the question. Hallucinations are especially dangerous when the false output sounds authoritative — users tend to trust confident-sounding answers, which is exactly when fabrications cause the most damage.
The defense is layered: ground answers in retrieved documents (RAG), require citations so users can verify, use a verification layer that double-checks claims against trusted sources, prefer larger models for high-stakes tasks, and design UIs that signal uncertainty rather than hiding it. No single technique eliminates hallucination entirely — but stacked together, they reduce it from "frequent" to "rare and detectable."
How Definable uses AI Hallucination
Definable reduces hallucinations through multiple layers: Knowledge Bases ground replies in your own documents with inline citations; the Workflow verification layer monitors every step for correctness; multi-model AI lets you cross-check answers across models when stakes are high; and every reply is auditable in the run log.
Frequently asked questions
How can I tell if an AI is hallucinating?
Look for citations. If the model can't point to a source for a specific claim, treat it as unverified. Cross-check facts against trusted references, especially for numbers, dates, names, and quotes.
Do bigger models hallucinate less?
Generally yes, but not always. Larger models have more knowledge but can still confabulate when they're uncertain. The most reliable defense is grounding (RAG) and citations, regardless of model size.
Does RAG eliminate hallucinations?
It reduces them significantly but doesn't eliminate them. A well-built RAG system grounds answers in retrieved content, but the model can still misinterpret or override that content. Citations let you verify.
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