A RAG model combines AI text generation with retrieval of external information, such as from databases or documents.

Where is the RAG model used?

For generating accurate, context-aware answers in AI search systems like chatbots, assistants, or semantic search engines.

What are the benefits of a RAG model?

RAG models provide up-to-date, verifiable answers and reduce hallucinations by grounding responses in real sources.

RAG-Modell - RAG Model – Retrieval-Augmented Generation

A RAG model (Retrieval-Augmented Generation) combines text generation from a language model with an external knowledge source, such as a database, document archive, or semantic search engine.

Unlike traditional AI systems that rely solely on their training data, a RAG model can retrieve relevant information during the request and include it in the response. This leads to more accurate, up-to-date, and verifiable answers.

In AI search systems like ChatGPT with Bing or enterprise bots connected to internal documents, RAG models ensure that answers are grounded in real sources rather than assumptions.

Combines language model with connected data source
Answers based on up-to-date information
Ideal for context-aware AI search
Reduces hallucinations by using real sources
Used in enterprise chatbots or AI search systems