Learn the key differences between three major techniques for enhancing large language models in this 18-minute educational video. Explore prompt engineering as a lightweight approach that guides model responses through carefully designed inputs without changing internal parameters. Understand how Retrieval-Augmented Generation (RAG) improves model outputs by incorporating relevant external documents at runtime, enabling informed answers even on topics not covered in training. Discover fine-tuning as a method for customizing models through additional training on domain-specific data, offering the highest level of specialization despite requiring significant resources. Compare the advantages of each approach: prompt engineering for speed and simplicity, RAG for accessing up-to-date knowledge, and fine-tuning for maximum customization.