s generate text based on statistical probabilities — they calculate which word or phrase is most plausible in a given context. They have no concept of truth or factuality. When a model has no well-founded answer, it invents one that sounds linguistically coherent.

Hallucinations manifest in various ways: invented sources and citations (the model names books, articles or studies that do not exist), false facts (incorrect dates, names, legal texts), unjustifiable conclusions and — dangerously in an enterprise context — the fabrication of contracts, policies or price lists that do not exist.

For enterprise use, hallucination is a critical problem: employees who rely on hallucinated answers make decisions on false grounds. In regulated areas (medicine, law, finance) this can have serious consequences.

RAG-based systems like Silent AI minimize hallucinations structurally: the model answers only based on source documents identified during retrieval. When no relevant source is available, the system signals this — instead of inventing an answer. Every response is linked to the source documents from which it was derived.

Frequently asked questions

RAG significantly reduces hallucinations by binding the model to concrete source documents. It cannot be completely eliminated, as the model always linguistically interpolates when formulating answers from sources. That is why source attribution is important: every Silent AI response references the documents from which it was derived — the user can directly verify the basis of the answer.
Linguistically, hallucinations are often difficult to identify — that is precisely the problem. Reliable indicators are: missing or untraceable source references, very specific numbers without evidence and answers that seem too perfectly tailored to the question. The best protection is an AI system that backs every answer with verifiable sources.