Large Language Models (LLMs) have revolutionized natural language processing, enabling advanced applications in language analysis, semantic search, and data retrieval. However, their deployment presents significant challenges, including precision limitations, hallucination risks, and scalability issues. This article explores strategies to address these challenges, focusing on Retrieval-Augmented Generation (RAG), vector semantic search, and optimization techniques to enhance LLM performance.