What does hallucination mean in an AI application?

Defines hallucination as unsupported or incorrect model output and connects it to evidence, task design, evaluation, and user-visible uncertainty.

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TL;DR

Hallucination usually means the model produces content that is incorrect, fabricated, or unsupported by the evidence it was expected to use. The wording may still sound confident because the model is generating a plausible continuation, not checking every claim against a source of truth. The exact definition depends on the task: an unsupported citation, an invented API method, and a summary that contradicts its document are related but measurable in different ways.

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A precise explanation of an overloaded term and practical controls that do not promise hallucination can be eliminated.

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Core answer
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Overview
Hallucination is a product risk only when it is defined against the task and available evidence rather than used as a label for every bad answer.
Hallucination usually means the model produces content that is incorrect, fabricated, or unsupported by the evidence it was expected to use. The wording may still sound confident because the model is generating a plausible continuation, not checking every claim against a source of truth. The exact definition depends on the task: an unsupported citation, an invented API method, and a summary that contradicts its document are related but measurable in different ways.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
I reduce the risk by narrowing the task, supplying authoritative context, asking for evidence where appropriate, validating structured facts, and allowing the system to say it lacks enough information. RAG can improve grounding but cannot guarantee it; retrieval may return the wrong passage and the model may ignore or misread it. For material decisions, application code verifies values against the system of record or requires human review.

Review signals

Use these to catch weak phrasing, shallow reasoning, and risky claims.

Common mistakesCandidateCommon mistakesMistakes that weaken the answer or signal shallow understanding.Open details

Details

Calling every undesirable response a hallucination.
Claiming RAG eliminates hallucination.
Asking the model to verify its own answer and treating that as proof.
Ignoring the harm difference between a draft and a state-changing decision.

What does hallucination mean in an AI application?

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