What is RAG (Retrieval-Augmented Generation)?

RAG (Retrieval-Augmented Generation) is an approach in natural language processing that combines generative models with mechanisms for searching and retrieving relevant information from external sources.

The idea is that instead of relying solely on the knowledge embedded in the model, the system first retrieves relevant documents or text fragments from a database or search index, and then uses this data to generate a more accurate and well-founded response.

Example of application:

• The user asks a question.
• The system searches for relevant documents in the knowledge base.
• The generative model forms a response based on the retrieved information.

This allows for improved quality of answers and relevance of information, especially when the knowledge base is constantly updated.