Author(s): Surya Maddula

Originally published on Towards AI.

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AI and Natural Language Processing are advancing at an incredible pace, and now more than ever, we need better and more RELIABLE ways to find and use information. As we've all experienced, traditional systems often struggle to answer our questions in the most relevant and contextually rich manner. Just take the example of Google and how you usually have to perform multiple searches to find out what you want to know.

That's where Retrieval Augmented Generation (RAG) and its more advanced version, RAG Fusion, come into play. Hopefully, over the next few minutes, you'll learn everything you need to know about RAG Fusion—how it works, its benefits, real-world uses, challenges, future possibilities, and example use cases.

RAG, also known as Retrieval Augmented Generation, is an AI framework that improves the quality and accuracy of responses generated by large language models (LLMs) by grounding them in external sources of knowledge, which is why the name retrieval augmented generation.

A brief overview of the different stages of rag processing:

First, we retrieve relevant information from an external knowledge base or data sources based on the user's query.Then, we append the retrieved information… Read the full blog for free on Medium.

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Published via Towards AI