The field of retrieval-augmented generation (RAG) is moving towards more efficient and reliable systems, with a focus on optimizing large language models (LLMs) for real-time applications. Researchers are exploring novel approaches to improve the performance of RAG systems, including the use of pairwise reranking and hybrid models. Additionally, there is a growing interest in decentralized RAG systems, which enable foundation models to utilize information directly from data owners while maintaining control over their sources. Blockchain-based systems are also being investigated for their potential to provide secure and transparent management of reliability scores and smart contracts. Noteworthy papers in this area include: EncouRAGe, which introduces a comprehensive Python framework for evaluating RAG systems. LLM Optimization Unlocks Real-Time Pairwise Reranking, which demonstrates significant latency reduction in pairwise reranking tasks. A Decentralized Retrieval Augmented Generation System with Source Reliabilities Secured on Blockchain, which presents a novel reliability scoring mechanism for decentralized RAG systems. Practical RAG Evaluation, which contributes a rarity-aware set-based metric and cost-latency-quality trade-offs for building production RAG systems.