This text introduces the concept of prompt engineering, explaining it as the process of crafting effective inputs for large language models (LLMs) to achieve accurate and desired outputs across various tasks. It covers several prompting techniques, such as zero-shot, few-shot, system, contextual, role, step-back, Chain of Thought (CoT), self-consistency, Tree of Thoughts (ToT), and ReAct, detailing how each guides LLM behavior. The text also discusses LLM output configuration options like temperature, Top-K, and Top-P, and provides best practices for prompt design, emphasizing simplicity, specificity, providing examples, and documenting attempts. Finally, it touches on code prompting capabilities, including writing, explaining, translating, and debugging code with LLMs, and briefly mentions multimodal prompting and automatic prompt engineering.