The Role of Structured Content in RAG

(TL:DR—structured content is of paramount importance if you want to fine-tune your LLM with accurate and trusted content) Large language models (LLMs) like GPT-3 have shown remarkable capabilities in generating text on a wide range of topics. However, these models are not without limitations. A major challenge is ensuring that the information generated is accurate and up-to-date, especially for rapidly changing or highly specialized domains. Enter Retrieval Augmented Generation (RAG)—RAG is a technique that allows LLMs to incorporate external information from a corpus of documents during the text generation process. By augmenting the model's knowledge with relevant information from a curated set of sources, RAG can potentially improve the accuracy, timeliness, and factual grounding of LLM output.