Building an end-to-end Retrieval- Augmented Generation (RAG) workflow
One of the most critical gaps in traditional Large Language Models (LLMs) is that they rely on static knowledge already contained within them. Basically, they might be very good at understanding and responding to prompts, but they often fall short in providing current or highly specific information. This is where RAG comes in; RAG addresses these critical gaps in traditional LLMs by incorporating current and new information that serves as a reliable source of truth for these models.