Why does AI sometimes invent facts, and how can I reduce hallucinations?

A language model generates a likely continuation from patterns learned during training and from the context supplied in the conversation. It is optimized to produce useful language, not to act as a perfect database. When evidence is missing, ambiguous, outdated, or internally conflicting, the model can produce a plausible statement that is not true.

Hallucinations are more likely when a prompt demands precision the model does not have. Examples include obscure names, exact quotations, current prices, detailed citations, niche technical behavior, or events after the training period. They also occur when the user assumes a false premise and the model cooperates instead of challenging it.

Reduce the risk by allowing uncertainty. Say that unknown facts should be marked unknown and that sources must not be invented. Ask the model to list assumptions and identify which claims require verification. For current or niche topics, require web search and direct links to the pages actually used.

Give the model authoritative material when possible. If the task concerns a policy, contract, manual, or dataset, provide that source and ask the model to stay within it. Request section or page references. This does not guarantee accuracy, but it makes unsupported claims easier to detect.

Break complex work into stages: extract facts, verify them, calculate or compare, then write the conclusion. Do not ask one prompt to discover evidence, resolve conflicts, perform calculations, and produce polished recommendations without showing intermediate support.

Finally, match verification effort to risk. Check every consequential fact in legal, medical, financial, safety, and operational decisions. For low-stakes creative work, factual precision may matter less. The goal is not to eliminate every possible error; it is to prevent confident language from receiving more trust than the evidence deserves.