RAG (Retrieval-Augmented Generation)

RAG, or Retrieval-Augmented Generation, is a hybrid AI framework that combines the generative capabilities of large language models (LLMs) with the real-time retrieval of information from external, authoritative knowledge bases. In operation, the system first runs the user's question through a search engine or vector database, then attaches the relevant results as context to the language model, which formulates its response based on these. The main advantage of RAG architecture is that it minimizes model hallucinations and enables the secure use of up-to-date, company-specific, or closed data without requiring the model to be retrained. This technology is critical in enterprise knowledge management systems where accuracy and data freshness are critical requirements.