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What is RAG

In the context of large language models (LLMs), RAG stands for Retrieval-Augmented Generation. It's a hybrid approach that combines the power of pre-trained language models with real-time retrieval of external information to improve the quality and relevance of generated responses.

How RAG Works:

• Retrieval: Before generating a response, the model retrieves relevant documents or information from a large external knowledge base, such as a database, web index, or other unstructured sources.
• Augmentation: This retrieved information is then used to provide context, augmenting the pre-trained language model’s internal knowledge.
• Generation: The LLM processes both the retrieved information and its own understanding to generate a more accurate, context-aware, and detailed response.