DeepRAG focuses on improving the decision-making process of large language models (LLMs) by effectively integrating a retrieval step. This approach fine-tunes the model's decision-making regarding when to retrieve external knowledge, contrasting with the Agent Workflow Memory (AWM) paper's reliance on in-context learning. The discussions in the comments center around potential enhancements of AWM to accommodate retrieval-augmented generation (RAG) systems and the implications of such integrations on performance metrics like answer accuracy and latency. There's a noted interest in setting up local development environments for implementing similar functionalities and concerns regarding the latency trade-offs in naive RAG systems.