Retrieval Augmented Generation aka RAG is a new paradigm in the world of Generative AI which allows AI systems to provide more contextual, accurate and personalised responses by combining the power of LLM with rich and proprietary data sets. These data sets can range from internal documents, databases, to APIs and research papers. This approach uplifts the capabilities of LLMs from providing generic responses to delivering domain specific responses.

This blog post (fourth in the Uncovering GenAI series) picks apart the RAG paradigm, and dives deeper. It explains the basics and then moves to exploring what realistic RAG systems look like.