Retrieval-Augmented Generation: State of the Art and Future Directions

Retrieval-Augmented Generation (RAG) remains crucial for addressing limitations of Large Language Models (LLMs), such as hallucinations and outdated knowledge, by integrating external retrieval systems. The text describes RAG's evolution from a simple linear design to a more robust, layered architecture in production systems.