What is AI Hallucination?

AI hallucination happens when a Large Language Model[LLM], mostly a GenAI chat/search tool gives a confident answer that isn’t grounded in its inputs or any reliable source. The model produces something that sounds right but isn’t supported by data.

It often stems from gaps or bias in training data, pattern-matching beyond the available context, or weak grounding in real-world facts.

Hallucinations break trust and create risk. A retail assistant might claim a jacket is waterproof when the catalog never says so, leading to returns; in healthcareAI reporting, an invented detail could mislead clinicians. The problem can also snowball when fabricated text is re-indexed and treated as truth elsewhere.

Example

A shopper opens a product page and asks, “Is this jacket waterproof?” The model, working only from the title “Storm Lite Jacket” and a few marketing lines, answers: “Yes, fully waterproof with sealed seams.” That’s a hallucination.

The actual spec sheet says “water-resistant, 5,000 mm; seams not taped.” When the assistant is grounded in the catalog (reading the spec fields directly), it replies: “This jacket is water-resistant, not waterproof. If you need waterproof, here are two options with 20,000 mm rating and taped seams in your size.” The difference is simple: ungrounded = confident guess; grounded = fact-based answer plus a helpful alternative.

How hallucinations are reduced in model performance?

In practice teams reduce hallucinations by tightening the model’s connection to source data (retrieval/grounding with citations), using higher-quality and better-scoped training sets, constraining outputs to schemas or allowed values, and keeping humans in the loop for high-stakes tasks. Continuous testing and monitoring are essential because data and behavior shift over time.

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