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Prompt Engineering for LLMs: The Art and Science of Building Large Language Model-Based Applications 1st Edition by John Berryman (Author), Albert Ziegler (Author)

Prompt Engineering for LLMs: The Art and Science of Building Large Language Model-Based Applications 1st Edition by John Berryman (Author), Albert Ziegler (Author)
Prompt Engineering for LLMs: The Art and Science of Building Large Language Model-Based Applications 1st Edition by John Berryman (Author), Albert Ziegler (Author)

Prompt Engineering for LLMs: The Art and Science of Building Large Language Model-Based Applications 1st Edition by John Berryman (Author), Albert Ziegler (Author)

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by John Berryman (Author), Albert Ziegler (Author)

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Product details

  • Publisher ‏ : ‎ O'Reilly Media
  • Publication date ‏ : ‎ December 31, 2024
  • Edition ‏ : ‎ 1st
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 280 pages
  • ISBN-10 ‏ : ‎ 1098156153
  • ISBN-13 ‏ : ‎ 978-1098156152
  • Item Weight ‏ : ‎ 1.08 pounds
  • Dimensions ‏ : ‎ 7 x 0.59 x 9.19 inches

Large language models (LLMs) are revolutionizing the world, promising to automate tasks and solve complex problems. A new generation of software applications are using these models as building blocks to unlock new potential in almost every domain, but reliably accessing these capabilities requires new skills. This book will teach you the art and science of prompt engineering-the key to unlocking the true potential of LLMs.

Industry experts John Berryman and Albert Ziegler share how to communicate effectively with AI, transforming your ideas into a language model-friendly format. By learning both the philosophical foundation and practical techniques, you'll be equipped with the knowledge and confidence to build the next generation of LLM-powered applications.

 

  • Understand LLM architecture and learn how to best interact with it
  • Design a complete prompt-crafting strategy for an application
  • Gather, triage, and present context elements to make an efficient prompt
  • Master specific prompt-crafting techniques like few-shot learning, chain-of-thought prompting, and RAG
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