This course provides a comprehensive overview of prompt tuning and prompt engineering techniques for optimizing the performance of large language models (LLMs). It covers the foundations of prompt engineering, including designing and refining input prompts to achieve desired outputs. The course explores core concepts such as zero-shot and few-shot prompting, principles of effective prompt design, and advanced techniques like Chain-of-Thought prompting and Retrieval-Augmented Generation (RAG). Additionally, it delves into prompt tuning as a parameter-efficient fine-tuning method and automated prompt optimization strategies.