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.

The pace at which GenAI has exploded and gone from novelty to necessity is also reflected in the landscape of tools, platforms, communities, breakthroughs, etc. The field is evolving so rapidly that staying up to date can easily feel like a full time job. Hence, this blog focuses on the current state of the ecosystem to help readers get started.

Many products are actively finding innovative use-cases for helping their customer base with Generative AI aka GenAI. As software engineers and architects, we are tasked to build the applications and integrations which sit behind the scenes powering these GenAI use-cases. A core belief that guides this approach is that establishing a well-formed understanding of a technology before integration helps to uncover its maximum benefit. For those working on a similar belief or in general like to go down the rabbit-hole, this blog is the first of the series to uncover the basics of GenAI (i.e., remove the "buzz" from the buzzwords).