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Prompt Engineering for Generative AI

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Details

  • Title: Prompt Engineering for Generative AI
  • Subtitle: Future-Proof Inputs for Reliable AI Outputs
  • Authors: James Phoenix and Mike Taylor
  • Publication Date: 2024
  • Publisher: O'Reilly
  • ISBN-13: 978-1098153434
  • Pages: 422
  • Amazon Rating: 4.4 stars
  • Goodreads Rating: 3.61 stars

Links: Amazon | Goodreads | Publisher | GitHub Project

Blurb

Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation.

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 architecture—and 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

Contents

  1. Five Pillars of Prompting
  2. Intro to Text Generation Models
  3. Standard Practices for Text Generation
  4. Advanced Techniques for Text Generation with Langchain
  5. Vector Databases
  6. Autonomous Agents with Memory and Tools
  7. Intro to Diffusion Models for Image Generation
  8. Standard Practices for Image Generation
  9. Advanced Techniques for Image Generation
  10. Building AI-powered Applications