Answers grounded in your own knowledge
Retrieval-Augmented Generation combines a large language model with your private knowledge base — retrieving relevant data and generating accurate, reliable, current answers while sharply reducing hallucinations.
What is RAG AG?
Retrieval-Augmented Generation (RAG) is an AI technology that combines a large language model (like GPT or Gemini) with an external knowledge base. It retrieves relevant data from the base and uses it to deliver accurate, reliable and current answers — while sharply reducing model errors and hallucinations.
How it works in practice
The RAG process has two main stages, executed in a fraction of a second every time you ask a question:
Retrieval
The system scans your private or organizational knowledge base (documents, websites, databases) and locates the data and documents most relevant to your question.
Generation
The system passes your question to the language model together with the precise retrieved information. The model reads the data and formulates a high-quality answer based only on that knowledge base.
Why it matters
- Accurate answers grounded in your own data, not the model's training memory
- Dramatically fewer hallucinations — every answer cites its source
- Always current — connect new documents and the base updates instantly
- Works in Hebrew, English, German and Arabic out of the box