What is an AI hallucina­tion?

AI hallucina­tion: definition and causes

The term describes model outputs that are linguistically flawless but incorrect in content. Typical examples are invented citations, wrong product data or court rulings that do not exist. What makes it tricky is that the tone stays confident: wrong answers sound just as convincing as right ones.

The cause lies in how the technology works. A large language model calculates which words are likely to follow; it does not check whether a statement is true. If the model lacks knowledge or suitable context, it fills the gap with something plausible. Contradictory or outdated training data and ambiguous questions also raise the risk.

How hallucina­tions can be reduced

They cannot currently be avoided completely, but they can be reduced significantly. The most effective step is to base the model on verified sources: with retrieval-augmented generation (RAG) it receives relevant documents and is supposed to answer only on that basis, ideally with a source reference so users can verify.

Add to that clear instructions (“If the answer is not in the documents, say so”), low randomness settings for factual questions, automatic review steps and human oversight for important content. Whether a system hallucinates shows up most reliably in tests with real questions and known answers.

Real-world example: a wrong promise in customer chat

An electronics retailer uses an AI chatbot. A customer asks about the warranty for a device, and the bot states a term that appears in no contract. The customer later relies on it. The case shows that companies must stand behind the statements of their systems, even if a model made them up.

After the incident, the bot was supplied only with the current warranty terms, was meant to hand over to service when information was missing, and was checked regularly with test questions. Commitments on prices, deadlines and goodwill have since no longer been left to the model. For contract questions an employee now always takes over, and it is visible in the chat that it is an AI.

Typical mistakes and imple­menta­tion

Common mistakes are letting a model answer technical questions without a data basis, passing on answers unchecked, or assuming that a better model no longer hallucinates. It is important to assess risk per use case: a text draft tolerates more uncertainty than legal information. Depending on the use, a notice to users is enough; in other cases a fixed approval is needed before every output.

Nano Concepts builds AI applications with source binding, answer limits and test procedures so that hallucinations are noticed early and do not reach the customer. Together we define where a model may formulate freely and where only verified content is allowed, and we set up test questions that run automatically after every change.

• Base answers on verified sources and show the sources
• Instruct the model not to guess when information is missing
• Take binding statements on prices and deadlines from systems, not from the model
• Run test questions with known answers regularly
• Require human approval for content with legal or financial effect