What are AI hallucinations?
Invented but plausible answers from an AI model: false information delivered with the same confident tone as true facts.
A hallucination is an answer the model makes up: a figure, a quote, a regulation or a reference that does not exist, delivered with the same confident tone as a verified fact. It is not an occasional bug: it is a structural property of language models, which generate the most probable text, not the most true. It happens because a language model does not consult a store of verified facts: it generates the most probable sequence of words given the context, and when the requested information is missing, it fills the gap by inventing rather than admitting it does not know. The risk is highest exactly on the questions a business cares about most, niche topics, recent data, precise figures and regulatory references, where a confidently delivered error can end up in a contract, a report or a decision before anyone has the chance to verify it.
Why it happens
An LLM does not consult a store of facts: it produces words coherent with the context. When the context does not contain the requested information, the model fills the gap by making things up, because it is trained to answer. Hallucinations increase on niche questions, recent data, precise numbers and regulatory references, which is exactly the terrain of business questions.
How companies manage them
Eliminating them entirely is not possible; reducing them to a manageable level is. The practical levers: ground answers in company documents with RAG (the model cites verifiable sources instead of going from memory), require the AI to state when it does not know, validate critical outputs with automated checks or human review, and curate source data quality, because a model grounded in wrong documents produces errors with a pedigree. The operating rule: never use unverified LLM output in processes with legal, financial or safety consequences.
Related terms
- RAG · A technique that grounds an LLM in your company data: it retrieves relevant documents and feeds them to the model before it answers.
- AI Agents (Agentic AI) · AI systems that go beyond answering: they plan, use tools and take actions autonomously inside your processes.
- Data quality · How fit your data is for its intended use: complete, correct, fresh and consistent across systems. Measured, not declared.
- Sycophancy · Sycophancy is an AI model's tendency to agree with the user even when wrong, instead of correcting them.
- AI slop · AI-generated content produced at volume without care: mediocre text, images and code polluting the web, inboxes and repositories.
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