LLM Prompt Engineering for Developers
The Art and Science of Unlocking LLMs' True Potential
Explore the dynamic field of LLM prompt engineering with this book. Starting with fundamental NLP principles & progressing to sophisticated prompt engineering methods, this book serves as the perfect comprehensive guide.
Key Features
- In-depth coverage of prompt engineering from basics to advanced techniques.
- Insights into cutting-edge methods like AutoCoT and transfer learning.
- Comprehensive resource sections including prompt databases and tools.
Book Description
"LLM Prompt Engineering For Developers" begins by laying the groundwork with essential principles of natural language processing (NLP), setting the stage for more complex topics. It methodically guides readers through the initial steps of understanding how large language models work, providing a solid foundation that prepares them for the more intricate aspects of prompt engineering.As you proceed, the book transitions into advanced strategies and techniques that reveal how to effectively interact with and utilize these powerful models. From crafting precise prompts that enhance model responses to exploring innovative methods like few-shot and zero-shot learning, this resource is designed to unlock the full potential of language model technology.
This book not only teaches the technical skills needed to excel in the field but also addresses the broader implications of AI technology. It encourages thoughtful consideration of ethical issues and the impact of AI on society. By the end of this book, readers will master the technical aspects of prompt engineering & appreciate the importance of responsible AI development, making them well-rounded professionals ready to focus on the advancement of this cutting-edge technology.
What you will learn
- Understand the principles of NLP and their application in LLMs.
- Set up and configure environments for developing with LLMs.
- Implement few-shot and zero-shot learning techniques.
- Enhance LLM outputs through AutoCoT and self-consistency methods.
- Apply transfer learning to adapt LLMs to new domains.
- Develop practical skills in testing & scoring prompt effectiveness.
Who this book is for
The target audience for "LLM Prompt Engineering For Developers" includes software developers, AI enthusiasts, technical team leads, advanced computer science students, and AI researchers with a basic understanding of artificial intelligence. Ideal for those looking to deepen their expertise in large language models and prompt engineering, this book serves as a practical guide for integrating advanced AI-driven projects and research into various workflows, assuming some foundational programming knowledge and familiarity with AI concepts.
Table of Contents
- Preface
- From NLP to Large Language Models
- Introduction to Prompt Engineering
- OpenAI GPT and Prompting: An Introduction
- Setting Up the Environment
- Few-Shot Learning and Chain of Thought
- Chain of Thought (CoT)
- Zero-Shot CoT Prompting
- Auto Chain of Thought Prompting (AutoCoT)
- Self-Consistency
- Transfer Learning
- Perplexity as a Metric for Prompt Optimization
- ReAct: Reason + Act
- General Knowledge Prompting
- Introduction to Azure Prompt Flow
- LangChain: The Prompt Engineer's Guide
- A Practical Guide to Testing and Scoring Prompts
- General Guidelines and Best Practices
- How and Where Prompt Engineering Is Used
- Anatomy of a Prompt
- Types of Prompts
- Prompt Databases, Tools, and Resources
- Afterword