The field of Large Language Models (LLMs) is witnessing significant advancements with the integration of Retrieval-Augmented Generation (RAG) techniques. RAG architectures are being designed to improve the accuracy and reliability of LLMs in various applications, including drug side effect retrieval, conversational agents, and document question answering. These architectures aim to address the limitations of traditional LLMs by incorporating external knowledge and reducing the risk of hallucinations. The use of RAG is also being explored in high-stakes domains such as legal and finance, where accurate and traceable information retrieval is crucial. Additionally, researchers are investigating methods to detect and filter out poisoned documents that can compromise the security of RAG pipelines. Overall, the field is moving towards developing more robust and scalable RAG-based solutions that can be applied to a wide range of applications. Noteworthy papers include: RAG-based Architectures for Drug Side Effect Retrieval in LLMs, which proposes two architectures, Retrieval-Augmented Generation (RAG) and GraphRAG, to integrate comprehensive drug side effect knowledge into a Llama 3 8B language model. DeRAG: Black-box Adversarial Attacks on Multiple Retrieval-Augmented Generation Applications via Prompt Injection, which presents a novel method that applies Differential Evolution (DE) to optimize adversarial prompt suffixes for RAG-based question answering.