LLM Prompt Engineering for Developers

LLM Prompt Engineering for Developers

by Aymen El Amri
Released May 2024
Publisher(s): Packt Publishing
ISBN: 9781836201731

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Book description

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

  1. Preface
    1. What Are You Going to Learn?
    2. To Whom is This Guide For?
    3. Join the Community
    4. About the Author
    5. The Companion Toolkit
    6. Your Feedback Matters
  2. From NLP to Large Language Models
    1. What is Natural Language Processing?
    2. Language Models
    3. Statistical Models (N-Grams)
    4. Knowledge-Based Models
    5. Contextual Language Models
    6. Neural Network-Based Models
      1. Feedforward Neural Networks
      2. Recurrent Neural Networks (RNNs)
      3. Long Short-Term Memory (LSTM)
      4. Gated Recurrent Units (Grus)
    7. Transformer Models
      1. Bidirectional Encoder Representations from Transformers (BERT)
      2. Generative pre-trained transformer (GPT)
    8. What’s Next?
  3. Introduction to Prompt Engineering
  4. OpenAI GPT and Prompting: An Introduction
    1. Generative Pre-trained Transformers (GPT) Models
    2. What Is GPT and How Is It Different from ChatGPT?
    3. The GPT models series: a closer look
      1. GPT-3.5
      2. GPT-4
      3. Other Models
    4. API Usage vs. Web Interface
    5. Tokens
    6. Costs, Tokens, and Initial Prompts: How to Calculate the Cost of Using a Model
    7. Prompting: How Does It Work?
    8. Probability and Sampling: At the Heart of GPT
    9. Understanding the API Parameters
      1. Temperature
      2. Top-p
      3. Top-k
      4. Sequence Length (max_tokens)
      5. Presence Penalty (presence_penalty)
      6. Frequency Penalty (frequency_penalty)
      7. Number of Responses (n)
      8. Best of (best_of)
    10. OpenAI Official Examples
    11. Using the API without Coding
    12. Completion (Deprecated)
    13. Chat
    14. Insert (Deprecated)
    15. Edit (Deprecated)
  5. Setting Up the Environment
    1. Choosing the Model
    2. Choosing the Programming Language
    3. Installing the Prerequisites
    4. Installing the OpenAI Python library
    5. Getting an OpenAI API key
    6. A Hello World Example
    7. Interactive Prompting
    8. Interactive Prompting with Multiline Prompt
  6. Few-Shot Learning and Chain of Thought
    1. What Is Few-Shot Learning?
    2. Zero-Shot vs Few-Shot Learning
    3. Approaches to Few-Shot Learning
      1. Prior Knowledge about Similarity
      2. Prior Knowledge about Learning
      3. Prior Knowledge of Data
    4. Examples of Few-Shot Learning
    5. Limitations of Few-Shot Learning
  7. Chain of Thought (CoT)
  8. Zero-Shot CoT Prompting
  9. Auto Chain of Thought Prompting (AutoCoT)
  10. Self-Consistency
  11. Transfer Learning
    1. What Is Transfer Learning?
    2. Inductive Transfer
    3. Transductive Transfer
    4. Inductive vs. Transductive Transfer
    5. Transfer Learning, Fine-Tuning, and Prompt Engineering
    6. Fine-Tuning with a Prompt Dataset: A Practical Example
    7. Why Is Prompt Engineering Vital for Transfer Learning and Fine-Tuning?
  12. Perplexity as a Metric for Prompt Optimization
    1. Avoid Surprising the Model
    2. How to Calculate Perplexity?
    3. A Practical Example with Betterprompt
    4. Hack the Prompt
  13. ReAct: Reason + Act
    1. What Is It?
    2. React Using Lanchain
  14. General Knowledge Prompting
    1. What Is General Knowledge Prompting?
    2. Example of General Knowledge Prompting
  15. Introduction to Azure Prompt Flow
    1. What Is Azure Prompt Flow?
    2. Prompt Engineering Agility
    3. Considerations before Using Azure Prompt Flow
    4. Creating Your First Prompt Flow
    5. Deploying the Flow for Real-Time Inference
  16. LangChain: The Prompt Engineer’s Guide
    1. What is LangChain?
    2. Installation
    3. Getting Started
    4. Prompt Templates and Formatting
    5. Partial Prompting
    6. Composing Prompts Using Pipeline Prompts
    7. Chat Prompt Templates
    8. The Core Building Block of LangChain: LLMchain
    9. Custom Prompt Templates
    10. Few-Shot Prompt Templates
    11. Better Few-Shot Learning with Example Selectors
      1. NGram Overlap Example Selector
      2. Max Marginal Relevance Example Selector
      3. Length-Based Example Selector
      4. The Custom Example Selector
      5. Few-Shot Learning with Chat Models
    12. Using Prompts from a File
    13. Validating Prompt Templates
  17. A Practical Guide to Testing and Scoring Prompts
    1. How and What to Evaluate in a Prompt
    2. Testing and Scoring Prompts with promptfoo
    3. promptFoo: Using Variables
    4. promptfoo: Testing with Assertions
    5. Integration of promptfoo with LangChain
    6. Reusing Assertions with Templates in promptfoo (Dry)
    7. Streamlining the Test with promptfoo Scenarios
  18. General Guidelines and Best Practices
    1. Introduction
    2. Start with an Action Verb
    3. Provide a Clear Context
    4. Use Role-Playing
    5. Use References
    6. Use Double Quotes
    7. Use Single Quotes When Needed
    8. Use Text Separators
    9. Be Specific
    10. Give Examples
    11. Indicate the Desired Response Length
    12. Guide the Model
    13. Don’t Hesitate to Refine
    14. Consider Looking at Your Problem from a Different Angle
    15. Consider Opening Another Chat (ChatGPT)
    16. Use the Right Words and Phrases
    17. Experiment and Iterate
    18. Stay Mindful of LLMs Limitations
  19. How and Where Prompt Engineering Is Used
    1. Creative Writing
    2. Content Generation, SEO, Marketing, and Advertising
    3. Customer Service
    4. Data Analysis, Reporting, and Visualization
    5. Virtual Assistants and Smart Devices
    6. Game Development
    7. Healthcare and Medical
    8. Story Generation and Role-Playing
    9. Business intelligence and analytics
    10. Image Generation
  20. Anatomy of a Prompt
    1. Role or Persona
    2. Instructions
    3. Input Data
    4. Context
    5. Rules
    6. Output
    7. Examples
  21. Types of Prompts
    1. Direct Instructions
    2. Open-Ended Prompts
    3. Socratic Prompts
    4. System Prompts
    5. Other Types of Prompts
    6. Interactive Prompts
  22. Prompt Databases, Tools, and Resources
    1. Prompt Engine
    2. Prompt Generator for ChatGPT
    3. PromptAppGPT
    4. Promptify
    5. PromptBench
    6. promptfoo
    7. Promptperfect: A Prompt Optimization Tool
    8. Aiprm for ChatGPT: Prompt Management and Database
    9. FlowGPT: A Visual Interface for ChatGPT and Prompt Database
    10. Wnr.ai: A No-Code Tool to Create Animated AI Avatars
  23. Afterword
    1. What’s Next?
    2. Your Feedback Matters

Product information

  • Title: LLM Prompt Engineering for Developers
  • Author(s): Aymen El Amri
  • Release date: May 2024
  • Publisher(s): Packt Publishing
  • ISBN: 9781836201731