RAG Resources: What We're Reading to Power Smarter Content Solutions

In this post, DCL developers curated a selection of insightful articles and resources that provide a general overview AND delve deeper into RAG technology and the role structured content plays in ensuring accurate, efficient, and reliable interactions with LLMs. Introducing RAG 2.0 Large language models (LLMs) struggle with knowledge-intensive tasks because they are limited by the information they have been exposed to during training. The RAG approach pretrains LLMs, fine-tunes, and aligns all components as a single integrated system, backpropagating through both the language model and the retriever to maximize performance. [READ ARTICLE] A Beginner's Guide to Building a Retrieval Augmented Generation (RAG) Application From Scratch Retrieval Augmented Generation, or RAG, is all the rage these days because it introduces some serious capabilities to large language models like OpenAI's GPT-4 - and that's the ability to use and leverage their own data.