In this educational video, Marina Danilevsky, a Senior Research Scientist at IBM Research, introduces the framework of Retrieval-Augmented Generation (RAG) as a solution to the limitations of large language models (LLMs). She discusses how RAG works by combining the generative capabilities of LLMs with a retrieval component that sources information from a content store, ensuring the model's responses are both accurate and current. The video highlights the importance of RAG in addressing common issues with LLMs such as outdated information and lack of credible sources, ultimately leading to more reliable and trustworthy AI-generated content.