Retrieval-augmented generation (RAG) is a new wave in AI development that allows language models to access relevant documents through external retrieval, enabling them to answer queries beyond their original training timeline and scope. RAG is based on the concept of grounding in evidence, where a retriever and generative model work together to solve problems, with the goal of optimizing cost-quality tradeoffs in enterprise systems. The system involves backpropagating into the query encoder to learn about specific documents, allowing for improved retrieval algorithms. While current implementations often use frozen RAG without adapting to provided documentation, future developments aim to incorporate backpropagation across the entire model, enabling more flexible and dynamic AI systems. As RAG advances, it has the potential to revolutionize language models by grounding knowledge in relevant information, preventing hallucination, and improving multimodal interactions with humans. The technology holds great promise for transforming industries, but still faces challenges such as hallucination, attribution, compliance, data privacy, and cost-quality trade-offs.