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Prompt Engineering for Generative AI by James Phoenix and Mike Taylor book cover at Nybookshub
O'Reilly guide to future-proof AI inputs and prompt engineering patterns

Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs – James Phoenix & Mike Taylor |9781098153434

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Product Description

Prompt Engineering for Generative AI is the definitive technical manual for anyone looking to go beyond basic chat interactions and master the architecture of Large Language Models (LLMs). Prompt Engineering for Generative AI by James Phoenix and Mike Taylor provides a structured, professional framework for designing, testing, and scaling prompts that deliver consistent, high-quality results. As AI moves from a novelty to a core business infrastructure, this book ensures you have the skills to build robust AI-driven applications.

About the Book

Prompt Engineering for Generative AI bridges the gap between casual AI use and professional engineering. Published by O'Reilly, this guide explores the "inner workings" of models like GPT-4, Claude, and Llama, explaining why certain inputs work better than others. The authors introduce the "Prompt Engineering Lifecycle," covering everything from initial design to automated evaluation and version control. It is a future-proof resource that focuses on universal principles of linguistics and logic, ensuring your skills remain relevant even as models evolve.

What You’ll Learn / Why Read

Prompt Engineering for Generative AI teaches you how to reduce hallucinations and improve the reliability of AI outputs through techniques like Few-Shot Prompting, Chain-of-Thought, and ReAct frameworks. You will learn how to build "prompt templates" that can be integrated into software products and how to evaluate AI performance using objective metrics. Furthermore, the book explores the ethical implications of prompt injection and how to secure your AI systems against adversarial inputs. This is a must-read for developers and product managers who want to lead the next wave of technological innovation.

Author Bio

James Phoenix and Mike Taylor are leading experts in the field of AI and data science. With extensive experience in training corporate teams and building AI-powered tools, they specialize in making the complex world of machine learning accessible and actionable for technical professionals.

Product Details

  • Author: James Phoenix, Mike Taylor

  • Publisher: O'Reilly Media

  • Language: English

  • Format: Paperback

  • ISBN-13: 978-1098153434

  • Genre: Computers / Artificial Intelligence / Software Engineering

  • Pages: 250+ Pages

Why Buy from Nybookshub

Prompt Engineering for Generative AI professionals choose Nybookshub because we provide 100% authentic O'Reilly editions, known for their high-quality paper and clear rendering of code snippets and diagrams. We know that in the fast-paced world of tech, your reference materials must be accurate and durable. Our global shipping network ensures that this critical AI guide reaches developers from Silicon Valley to Bangalore without delay. At Nybookshub, we are committed to equipping the global workforce with the high-level knowledge needed to shape the future of technology.

Questions & Answers

Is this book too technical for non-programmers? While it contains code examples (Python/JSON), the core principles of prompt design are highly valuable for anyone working with AI in a professional capacity.

Does Nybookshub ship this O'Reilly edition globally? Yes, we offer fast, tracked international shipping to ensure you stay at the forefront of the AI revolution.

Does it cover specific models like GPT-4 or Gemini? Yes, it uses current models as examples while focusing on the underlying principles that apply to all LLMs.

Is this a "how-to" for ChatGPT or an engineering book? It is an engineering book focused on building reliable systems, though the skills will certainly make you a "pro" at using tools like ChatGPT.

Does it include information on RAG (Retrieval-Augmented Generation)? Yes, it discusses how prompt engineering plays a crucial role in successful RAG implementations.

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