LLM hallucinations and how to limit them in practice

A language model does not tell the truth or a lie — it predicts the most probable next word. Most of the time that produces a useful answer; sometimes it produces confident nonsense. This phenomenon is called hallucination, and in production it is a risk to be managed actively.

What a language model hallucination is

A model has no internal "database of facts". It works from patterns in its training data, and when it lacks support it fills in plausible-sounding text. The problem is not that it gets things wrong — it is that it gets them wrong confidently and without warning.

What can be done about them

Hallucinations cannot be eliminated entirely, but they can be reduced sharply with a combination of techniques:

• Grounding in data (RAG): the model answers from supplied material, not from memory, and can cite its source.
• Structured output: instead of free text we require a precise format that can be checked by machine.
• Validation: the output passes through control rules before it is used — numbers must add up, references must exist.
• Evaluations: on a set of real examples we continuously measure how often the model errs.

A human as the last safeguard

For sensitive decisions we never rely on the model alone. Wherever a mistake has a cost, a human checkpoint stays in the process. Trust in AI does not come from believing it blindly, but from knowing where its limits are — and designing the system around them.

How we handle it in our solutions

We do not build on the assumption that a model will not get things wrong. We build systems where a mistake is catchable: the model returns structured data that can be validated, answers are tied to a specific source, and where an error costs something a person stays in the process.

If you are weighing an AI feature where a wrong answer would hurt, write to us. There is more on our services page.