Retrieval-Augmented Generation (RAG)

What is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation (RAG) combines a language model with a search across external documents or databases. Instead of answering only from its training knowledge, the model incorporates current and company-specific content into its response.

How does RAG work?

For a query, relevant documents are first retrieved from a knowledge base, often via a vector search using embeddings. The language model then generates the answer using the retrieved documents as context.

Why is RAG relevant for companies?

RAG brings current, company-owned knowledge into AI answers without the model having to be retrained. It reduces hallucinations, and the sources used remain traceable.

RAG and data protection

With RAG, the knowledge source often contains sensitive company data. What matters, therefore, is where this data resides, who can access it and whether existing permissions are enforced.

How SecureCloud supports RAG scenarios

Through the MCP server, AI applications can access files stored in SecureCloud in a controlled way and use them as a knowledge source. The permission model is adopted, access is logged, and the data stays in Germany, GDPR-compliant.

What is the difference between RAG and fine-tuning?

With Retrieval-Augmented Generation (RAG), the model retrieves knowledge at runtime from an external source without being retrained. With fine-tuning, the model itself is further trained on additional data.

Does RAG reduce hallucinations?

Yes. Because the answers are based on concretely retrieved sources, the risk of fabricated statements falls, and the sources used remain traceable.

Where does the data reside with RAG?

The data resides in the knowledge source used, such as a document store. Where that source is operated and who can access it is decisive for data protection.