The need to customize Large Language Models (LLMs) for particular tasks and domains is rapidly increasing as these models become essential to contemporary AI systems. This is made possible by two main methods, each with its own advantages: Fine-Tuning and Retrieval-Augmented Generation (RAG). This talk offers a clear, practical comparison of both methods. We’ll explore real-world use cases, trade-offs, and hands-on demos showing how to build and evaluate simple fine-tuned and RAG-based models. Attendees will leave with a solid understanding of when to fine-tune, when to retrieve, and how to combine both effectively.