Summary:
- Defines Retrieval-Augmented Generation (RAG) as a framework that enhances Large Language Models by integrating external, real-time data sources.
- Explains the technical workflow of RAG, including the retrieval of relevant documents and the subsequent generation of context-aware responses.
- Highlights the benefits of RAG in reducing AI "hallucinations" and ensuring information accuracy by grounding model outputs in verified datasets.