Description
Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs is a book authored by James Phoenix and Mike Taylor, published by O'Reilly Media. It offers a detailed exploration of prompt engineering, a set of principles designed to optimize the interaction with generative AI models like large language models (LLMs) and diffusion models. These models, including popular ones like ChatGPT and Stable Diffusion, have been trained on extensive datasets from the internet, enabling them to contribute to a wide array of tasks.
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The book aims to provide readers with a solid foundation in generative AI, focusing on practical applications of these models. It addresses common challenges faced by developers when integrating LLMs and diffusion models into workflows, particularly the difficulty in obtaining reliable results for automated systems. The authors explain how transforming application problems into document completion problems within the model training domain can lead to more effective AI model requests.
Readers will learn about the structure of the interaction chain between AI models and applications, including the fine-grained steps involved. The book also covers the influence of model architecture on interaction strategies and how these principles can be applied in domains such as natural language processing, text and image generation, and code development.
With 422 pages, this first edition is set to be released on June 25, 2024. It is designed for developers and professionals looking to harness the power of generative AI in their work, providing insights into empowering AI to work effectively and reliably.
- CategoryPrompt Engineering Tools
Book Details
Comprehensive guide to prompt engineering
Focus on large language models and diffusion models
Practical applications in various domains
Strategies for reliable AI outputs
Detailed interaction chain structure
Influence of model architecture
Application problem transformation
Published by O'Reilly Media
Who This Book Is For
- AI model integration
- Natural language processing
- Text generation
- Image generation
- Code development
