As enterprises race to integrate Generative AI into their workflows, the “hallucination” problem remains a significant barrier to entry. To solve this, developers generally look toward two primary methodologies: Retrieval-Augmented Generation (RAG) and Fine-Tuning. However, choosing the wrong approach can lead to wasted computational budgets and stagnant performance. This article provides a comprehensive analysis of both techniques, evaluating them based on data freshness, technical complexity, and cost-efficiency, ultimately offering a decision framework for modern AI implementation.