Recent advancements in Retrieval-Augmented Generation (RAG) research are making significant strides in the field of artificial intelligence. RAGLAB, a new open-source library, has been introduced to standardize RAG research and enable fair comparisons between six existing RAG algorithms across ten benchmarks. This library features a modular architecture for each RAG component and standardizes key experimental variables such as generator fine-tuning. Additionally, NVIDIA has published a new research paper titled 'In Defense of RAG in the Era of Long-Context Language Models,' which argues for the continued relevance of RAG despite the rise of long-context windows in language models. Various projects and tutorials, including those by NVIDIA NIM and LangChainAI, are also exploring the practical applications of RAG in customer support and documentation enhancement. Other key entities in this space include AI Agents and Agentic RAG.