Large Language Models and generative AI seemingly took the world by storm in 2023. But their creation was many years in the making, with their technological roots stretching back over a decade to advancements in neural networks and deep learning created in the mid-2010s. And just as in those early days, effective contemporary use of generative artificial intelligence relies heavily on the incisive deployment of prompts. In The Quick Guide to Prompt Engineering, celebrated futurist, thought leader, and leadership and technology commentator Ian Khan delivers a concise and insightful discussion of how to begin designing and using prompts in a variety of Large Language Models. In the book, you'll explore how to understand generative artificial intelligence and how to use prompt engineering in various industries. You'll also find incisive explanations of the future of artificial intelligence, as well as illuminative case studies and hands-on exercises that provide you with a thorough understanding of how to engineer prompts that generate maximal utility. The author also presents a helpful glossary of terms and recommended resources for further learning. Khan describes the basics of generative artificial intelligence, explaining its origins and the role played by effective prompts (or inputs) into the results generated by the technology. He includes a step-by-step guide to creating effective prompts, explaining why some work so well and others fail to achieve the desired results. The book explains many relevant aspects of the ethics of good prompt engineering, explaining how to avoid some of the most commonly encountered pitfalls and mistakes. You'll learn how to generate both images and text from popular Large Language Models including DALL-E and ChatGPT, Midjourney, Google Bard, DeepArt, RunwayML, and more. You'll even discover how to create AI-generated video with VQ-VAE. Engaging and informative, this book is perfect for anyone with an interest in one of the latest and most useful technological advancements to be publicly deployed.