Learn about the different approaches to fine-tuning generative AI models in this 17-minute educational video that covers four key fine-tuning methodologies. Explore full fine-tuning as the foundational approach, then dive into parameter efficient fine-tuning (PEFT) techniques including Low Rank Adaptation (LoRA) and adapter tuning methods that reduce computational requirements while maintaining model performance. Understand instruction fine-tuning for improving model responses to specific prompts and commands, and discover reinforcement learning from human feedback (RLHF) as an advanced technique for aligning AI models with human preferences and values. Gain practical knowledge of when and how to apply each fine-tuning method depending on your specific use case, computational resources, and desired outcomes in generative AI applications.