Large language models (LLMs) are impressive, but they sometimes hallucinate, meaning they make things up. Retrieval Augmented Generation (RAG) helps ground LLMs in reality by letting them access and process external documents. But RAG isn't perfect. If the AI's search query is too vague, it won't find the right documents, leading to more hallucinations. This new research introduces a clever technique called Query Optimization using Query expAnsion (QOQA). Imagine an AI trying to answer a question by searching through a massive library. QOQA helps the AI formulate sharper search queries. First, it takes an initial query and retrieves a few documents. Then, it uses those documents to generate several rephrased, more specific queries. QOQA scores these new queries based on how well they align with the retrieved documents. This process repeats, refining the query with each iteration, leading to increasingly accurate searches. The result? The AI finds more relevant information, leading to more accurate answers and fewer hallucinations. This method isn't just a theory. Experiments show QOQA boosts document retrieval accuracy by an average of 1.6%, a noticeable improvement in the world of AI. This research is a significant step toward more reliable, less hallucinatory AI. As this technology evolves, we can expect even smarter query optimization techniques to further enhance AI-powered search and generation.