In this comprehensive video, AWS expert Emily Webber explores prompt engineering and fine-tuning techniques for pre-trained foundation models. She covers zero-shot, single-shot, and few-shot prompting, as well as instruction fine-tuning and parameter-efficient methods. The video includes a hands-on demonstration using SageMaker JumpStart to fine-tune GPT-J 6B on SEC filing data, showcasing the power of these techniques for various NLP tasks like summarization, classification, and translation. Webber emphasizes the importance of using instruction-tuned models and provides practical tips for improving model performance through prompt engineering and fine-tuning. This video is an essential resource for developers and data scientists looking to leverage generative AI capabilities on AWS.