Large language models (LLMs) are impressive, but sometimes they hallucinate or give inconsistent answers. Retrieval Augmented Generation (RAG) helps by letting LLMs use external information, like a student consulting a textbook. But how do you make sure the LLM uses the *right* information? Existing methods struggle to balance giving LLMs relevant information while ensuring that information isn't redundant or contradictory. Enter SMART-RAG, a new method that uses math (specifically, Determinantal Point Processes) to make AI smarter. SMART-RAG prioritizes helpful, non-conflicting information when feeding data to an LLM, leading to better answers. Imagine an LLM trying to answer a medical question. One source says treatment X is 50% effective, while another claims it's useless. SMART-RAG would detect this conflict and prioritize the most reliable source, preventing the AI from giving a confused answer. Tested on various question-answering tasks, SMART-RAG consistently outperformed other methods, especially when fact verification was critical. By making sure the LLM has consistent, non-redundant information, SMART-RAG makes the whole system more reliable, without any extra training required. While promising, challenges remain. Checking for conflicts between sources is computationally expensive. The way sentences are currently grouped might miss some important context. However, SMART-RAG is a significant step towards making LLMs smarter and paves the way for better AI assistants, educational tools, and more.