Prompt Engineering for Generative AI
āĻāĻ āĻāĻŦā§āĻāĻāĻ¨ā§° āĻŦāĻŋāĻˇā§ā§
With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI.
Learn how to empower AI to work for you. This book explains:
- The structure of the interaction chain of your program's AI model and the fine-grained steps in between
- How AI model requests arise from transforming the application problem into a document completion problem in the model training domain
- The influence of LLM and diffusion model architectureâand how to best interact with it
- How these principles apply in practice in the domains of natural language processing, text and image generation, and code
āĻŽā§āĻ˛ā§āĻ¯āĻžāĻāĻāĻ¨ āĻā§°ā§ āĻĒā§°ā§āĻ¯āĻžāĻ˛ā§āĻāĻ¨āĻžāĻ¸āĻŽā§āĻš
āĻ˛āĻŋāĻāĻā§° āĻŦāĻŋāĻˇāĻ¯āĻŧā§
James Phoenix has a background in building reliable data pipelines for marketing teams, including automation of thousands of recurring marketing tasks. He has taught 40+ Data Science bootcamps for General Assembly.
Mike Taylor built and ran a 50-person marketing agency, including working on innovation projects with Unilever, Nestle, and Facebook. Over 300,000 people have taken his marketing courses on LinkedIn Learning.
