Large Language Models (LLMs) have shown remarkable abilities, but they sometimes struggle with complex reasoning tasks, especially those requiring information not contained within their internal knowledge. Think of it like trying to solve a mystery without all the clues. Retrieval-Augmented Generation (RAG) helps by providing LLMs with access to external information, like giving a detective access to a database of evidence. But even with RAG, efficiently sifting through multiple sources remains a challenge. This is where new research on Multi-Source Retrieval-Augmented Generation comes in. Researchers have developed a framework called MSPR, which goes beyond simply retrieving information. It acts like a super-sleuth, strategically choosing *when* to look for clues, *what* clues to search for, and *where* to look for them—be it in a reliable local database (like a police archive) or the vast, but sometimes less trustworthy, web. MSPR prioritizes exploring a high-quality, curated source first (think expert witness testimony) before venturing into the sprawling web. This approach helps ensure the LLM receives reliable information early on, forming a strong foundation for reasoning. MSPR also incorporates a feedback mechanism. After generating an answer, it evaluates its own work, much like a detective reviewing their case notes. If the answer seems incomplete or shaky, MSPR dives back into the information sources, specifically the web, to find missing pieces. Tests on challenging multi-hop question-answering datasets show that MSPR significantly outperforms existing methods. This research offers a promising direction for improving LLM reasoning capabilities. By optimizing the retrieval process and incorporating self-evaluation, LLMs can move closer to true, human-like reasoning, holding the potential to revolutionize fields like research, information retrieval, and even creative writing.