Discover the potential of retrieval augmented generation (RAG) with NVIDIA technologies. RAG systems combine information retrieval and generative models by retrieving relevant document passages from a large corpus, and then use them as context for generating detailed answers. We'll cover the design of end-to-end RAG systems, including data preparation and retriever and generator models. We'll showcase an example of RAG system using NVIDIA TensorRT-LLM and NeMo. We'll cover RAG models evaluation and customization for specific tasks.