Unlike traditional generative models that rely solely on pre-trained knowledge, RAG dynamically fetches and integrates external information before generating answers.
This approach is particularly useful in scenarios where up-to-date or niche information is needed, as it allows the model to 'retrieve' relevant documents from vast databases and use them as context.
Applications include customer support, where RAG can pull the latest product information, or healthcare, where it can source recent medical research to provide accurate guidance. The RAG framework’s hybrid method of coupling retrieval and generation offers greater depth, precision, and adaptability for knowledge-intensive AI tasks.