AI hallucination
An AI hallucination is when a model produces a confident, fluent, but factually incorrect or fabricated response.
An AI hallucination is a response produced by a generative model that is fluent and confident but factually incorrect or fabricated. The term applies to text (made-up citations, wrong policy answers), code (invented APIs), and any other domain where the model's output diverges from reality.
In context
Hallucinations are not bugs in the traditional sense, they are a consequence of how LLMs generate text. The model produces the most-likely token sequence given context, which is not the same as the most-true token sequence. When those diverge, the output is fluent and wrong at the same time.
Published 2025 benchmarks: ungrounded LLMs hallucinate in 15-30% of customer-service responses. A peer-reviewed 2025 Taylor & Francis study found hallucinations in 31.4% of real-world interactions, rising to 60% in complex domains. Production AI systems show 63% experiencing dangerous hallucinations within their first 90 days.
Mitigation strategies, RAG, system prompts, verification pipelines, real-time monitoring, NeMo-class guardrails, collectively cut risk by 71-89% in published benchmarks. None reduces it to zero. The metric that matters in production is recoverability: detection, attribution, and closure of the wrong answer before customer impact compounds.
How Auralis uses AI hallucination
Auralis Audit scores every closed conversation for accuracy and recoverability. Detected errors trigger candidate KB-gap articles, drafted by the Auralis team and reviewed by the customer, live within the week. The closed loop is what keeps the production hallucination tail manageable.