What are the main components of a RAG model? A rag model generally includes a retrieval system, a knowledge source, and a generative language model. The retrieval component finds information related to the user’s question, often from documents, databases, or vector stores. That information is passed to the language model as context, allowing it to generate a response based on the retrieved material. This architecture is useful for applications that need domain-specific knowledge. It can also make AI tools easier to update because new information can be added to the knowledge source without completely retraining the language model. https://dataqix.com/retrieval-augmented-generation/