The research landscape in Retrieval-Augmented Generation (RAG) systems is evolving rapidly, with a strong focus on enhancing both the retrieval and generation components to improve overall system performance. Recent developments emphasize the importance of hybrid retrieval methods, which combine vector search with keyword-based approaches, demonstrating significant improvements in retrieval accuracy. Additionally, there is a growing recognition of the critical role of prompt engineering in optimizing RAG systems, with custom-prompted agents showing consistent enhancements in response quality. Beyond answerable queries, there is a new emphasis on evaluating and improving the handling of unanswerable queries, which is crucial for developing more robust systems. In vertical domains such as finance, the introduction of comprehensive benchmarks is providing deeper insights into the performance variations of RAG systems across diverse topics and tasks, highlighting opportunities for further improvement. Furthermore, the integration of RAG with domain-specific ontologies is emerging as a promising approach to enhance the reliability and accuracy of question-answering systems, particularly in education and cybersecurity. These advancements collectively push the boundaries of RAG systems, making them more versatile, reliable, and effective across various applications.