RAG-Survey

RAG-Survey is an open-source research repository that collects and organizes academic papers related to retrieval-augmented generation (RAG) systems used in modern AI applications. Retrieval-augmented generation combines large language models with external knowledge retrieval systems to improve factual accuracy and contextual understanding. The repository functions as a curated catalog of research papers categorized according to a taxonomy proposed in a related survey paper on RAG methods. It organizes literature into multiple areas including foundational RAG models, architectural improvements, and application-specific implementations. Because the field is evolving rapidly, the repository is continuously updated with newly published research and emerging techniques. The resource is intended to help researchers and practitioners quickly explore the RAG ecosystem and understand the relationships between different approaches.