RAG Foundry: Enhance LLM Retrievers

Full Article: 2408.02545 (arxiv.org)

The rise of large language models (LLMs) has revolutionized the way we interact with data, enabling applications from natural language understanding to content generation. However, while these models are incredibly powerful, they have limitations, especially when it comes to recalling specific, up-to-date information. This is where Retrieval-Augmented Generation (RAG) systems come into play. We’ll explore what RAG systems are, delve into the recently proposed RAG FOUNDRY framework, and discuss the implementation challenges and advantages that make these systems a game-changer.

Understanding Retrieval-Augmented Generation (RAG) Systems

What are RAG Systems?

At their core, RAG systems are a hybrid approach that combines the strengths of two distinct technologies: information retrieval and generative models. Traditional generative models, like GPT, create content by predicting the next word in a sequence based on the input they receive. However, they rely solely on the data they were trained on, which can lead to outdated or inaccurate responses, especially when dealing with highly specific or recent information.

RAG systems mitigate this issue by integrating a retrieval mechanism into the generation process…