Classic s (LLMs) answer exclusively based on their training data. This creates two well-known problems: outdated knowledge (the model only knows its training cutoff) and hallucinations (the model invents plausible-sounding answers when it has no well-founded response).

RAG solves both problems structurally: when a question arrives, the system first searches a defined dataset — internal documents, wikis, databases — for relevant text passages. These passages are passed together with the question as context to the language model. The model then answers exclusively based on this retrieved content, not on its general training knowledge.

For enterprise use, RAG is critical for two reasons: first, source data stays within the organization — no company data is transferred to external AI services. Second, when RAG is combined with access rights management (AD/LDAP), the system can only access content for which a user has read permissions.

Silent AI is built on a RAG architecture over internal enterprise data. Connectors for Microsoft 365, SharePoint, Confluence, file servers and other sources feed the internal knowledge database. LLM inference runs entirely locally on dedicated GPU hardware — no prompt, no token leaves the network.

Frequently asked questions

RAG forces the language model to formulate answers exclusively based on retrieved source documents. When no relevant passage is found, the system returns that no answer is available — instead of inventing one. Hallucinations occur when a model answers without source binding; RAG structurally eliminates this case.
In a RAG implementation with AD/LDAP integration, access rights of the requesting user are verified when retrieving source documents. Documents for which no read access exists are not passed to the model's context — and therefore cannot flow into the answer. Users never see content they would not normally have access to.
No. RAG extends an existing language model with a retrieval mechanism — the model itself remains unchanged. This is a key advantage over fine-tuning: the dataset can be updated at any time without retraining the model.