Large language models (LLMs) are impressive, but they sometimes struggle with complex reasoning. Think of multi-hop question answering, where an LLM needs to connect the dots between different pieces of information to arrive at the correct answer. This is where RAG-Star comes in. This innovative approach supercharges LLM reasoning by combining the power of retrieval augmented generation (RAG) with a clever search strategy. Imagine the LLM brainstorming different paths to the answer, like exploring a maze. RAG-Star uses external knowledge, like a map, to guide this exploration, ensuring the LLM stays on track and avoids dead ends. Specifically, it uses a technique called Monte Carlo Tree Search (MCTS) to plan intermediate steps, asking sub-questions and generating potential answers. The brilliance of RAG-Star lies in its verification process. It retrieves relevant information and uses it to check the LLM's reasoning at each step, providing feedback and even refining incorrect answers. This continuous feedback loop helps the LLM learn from its mistakes and generate more accurate and coherent reasoning paths. Experiments with both closed and open-source LLMs demonstrate that RAG-Star significantly boosts performance on challenging multi-hop question answering datasets. This research opens exciting possibilities for improving LLM reasoning, particularly in knowledge-intensive tasks. However, challenges remain, including the computational cost of the search process and the need for high-quality reward models to guide the LLM effectively. Future research could explore more efficient search algorithms and alternative reward modeling strategies to further refine this promising approach.