Recent advancements in Retrieval-Augmented Generation (RAG) systems focus on enhancing reasoning capabilities and retrieval accuracy for large language models (LLMs). The CoT-RAG approach integrates chain of thought reasoning with knowledge graph-driven retrieval to improve LLM reasoning. Secure Multifaceted-RAG enhances enterprise applications by combining internal documents, expert knowledge, and filtered external LLM data to ensure security. A GraphRAG pipeline combining Qdrant's semantic search with Neo4j's symbolic reasoning addresses multi-hop reasoning challenges in connected knowledge domains. ApertureDB introduces a hybrid graph and vector-based RAG system, offering 2 to 10 times higher k-nearest neighbor throughput for faster and smarter AI agent retrieval. Evaluation frameworks have been proposed to assess RAG systems on component performance, factuality, safety, and computational efficiency. AlignRAG tackles reasoning misalignments between model predictions and retrieved evidence through iterative critique-driven alignment. Additionally, Collab-RAG improves handling of complex multi-hop questions by decomposing them into simpler sub-questions using a small language model to enhance retrieval for larger LLMs.