The Augmented Developer
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The Augmented Developer

Code Smarter, Not Harder

About the Book

The Augmented Developer is a comprehensive guide to AI-assisted coding. It covers everything from the use of AI in code completion, generation, refactoring, security, and bug detection, to the creation of custom model chains and AI assistants.

The guide dives into advanced prompt engineering techniques and best practices used to maximize the potential of AI in coding. It also explores the ethical and technical implications of using generative AI in coding. Moreover, it looks into the future impacts of AI in coding and its potential to change the developer role as we know it.

Across the chapters of The Augmented Developer, you will:

  • Discover the Fusion of Pair Programming and AI: Learn how AI-assisted coding is reshaping the dynamics of pair programming and remote work. Understand how these tools not only support but also enhance the overall coding experience.
  • Unpack GitHub Copilot: Dive into the specifics of GitHub Copilot, from its foundational AI training to its real-world application in coding. This section breaks down its features, pricing, and setup, making it practical for you to start using it in your projects right away.
  • Learn How to Use GitHub Copilot Effectively: Master the art of using GitHub Copilot to boost your productivity. From writing code, debugging, refactoring, security hardening, unit testing, documentation, code reviews, pull requests, context-aware prompting, and more. You'll gain a deep understanding of how to leverage GitHub Copilot to its full potential.
  • Explore a Spectrum of AI Coding Assistants and Agents: Get acquainted with the broad landscape of AI coding tools available today: Tabnine, DeepCode, fauxpilot, privy, aider, Devin, SWE-Agent, Codeium, Tabby, CodeGeeX, Amazon CodeWhisperer, AskCodi, Blackbox AI, Bito, and many more.. We'll compare their features, assess their impacts, and discuss their potential applications.
  • Master Prompt Engineering for Developer Productivity: Learn how to write better prompts for GitHub Copilot, ChatGPT and AI coding assistants. This section will guide you through the process of crafting prompts that yield the best results. From Few-Shot Learning, Chain of Thoughts, Self Consistency, Tree of Thoughts, Autonomous Agents, General Knowledge Prompting, and more, you'll gain a deep understanding of how to maximize the potential of AI in coding using the right prompts.
  • Understand the Best Practices of Prompt Engineering: Acknowledge the best practices of prompt engineering and how you can apply them with the techniques you've learned. This section will provide you with a clear roadmap to follow when writing prompts for AI coding assistants.
  • Build Your In-house Developer Productivity Tools: Learn how to use tools like OpenAI APIs, LangChain, Chroma, and others to code your own AI-powered developer productivity tools. This section will guide you through the process of coding your custom AI assistants, step by step.
  • Understand Advanced Concepts in Generative AI: Gain a clear understanding of useful concepts such as RAG, vector databases, embeddings, tokens, and more.
  • Discover Advanced LLM Orchestration Tools: Immerse yourself in the world of advanced LLM orchestration and workflow tools. From DiFy and Flowise AI to LLMStack and others, you'll receive an overview of the best tools to help you create your own LLM-based solutions in a matter of minutes.
  • Understand the Ethical and Technical Implications of AI in Coding: Gain a clear perspective on these implications and answer the most pressing questions about AI in coding. What's controversial about AI in coding? Can we really trust AI to write code? Will AI replace developers? And more.

After reading this book, you'll possess the practical knowledge and tools needed to effectively use AI in coding, thereby enhancing your productivity as a developer.

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    • Computers and Programming
    • GPT
    • Programming Cookbooks
    • Python
    • Software Engineering
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About the Author

Aymen El Amri
Aymen El Amri

Aymen El Amri is an author, entrepreneur, trainer, and polymath software engineer who has excelled in a range of roles and responsibilities in the field of technology including DevOps & Cloud Native, Cloud Architecture, Python, NLP, Data Science, and more.

Aymen has trained hundreds of software engineers and written multiple books and courses read by thousands of other developers and software engineers.

Aymen El Amri has a practical approach to teaching based on breaking down complex concepts into easy-to-understand language and providing real-world examples that resonate with his audience.

Some projects he founded are FAUN,, and Marketto. You can find Aymen on Twitter and Linkedin.

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Table of Contents

    • Preface
      • What Are We Going to Cover?
      • Who Is This Book For?
      • The Companion Toolkit
      • Join the Community
      • About the Author
    • Pair Programming, Remote Work, and AI-Assisted Coding
    • GitHub Copilot: A Step-by-Step Comprehensive Guide
      • What is GitHub Copilot?
      • How was GitHub Copilot trained?
      • The 2x Developer: 50% Faster Coding
      • Pricing and Availability
      • Supported Programming Languages
      • Supported IDEs and Code Editors
      • Installation and Setup
        • Visual Studio Code
        • Visual Studio
        • JetBrains IDEs
      • GitHub Copilot Core Features
        • Code Completion
        • From Comment/Prompt to Code
        • From Documentation to Code
        • From Code to Documentation
        • Automate Repetitive Patterns
        • Create Dummy Data
        • Predict the Output
        • Chat with Your Code
        • From Code to Diagram
        • Error Handling and Problem Solving
        • Transpiling Code
        • Code Review
        • Getting More Than One Suggestion
      • GitHub Copilot Chat
        • Prerequisites and Setup
        • Chat Windows vs. Inline Chat
        • Chatting with Copilot
        • Threads and Context
        • Asking How to Do Something in the Terminal
        • Asking Questions About VSCode
        • Asking Questions About Your Workspace
        • Explaining Code
        • Fixing Problems
        • Bootstrapping a New Project
        • Creating Tests
        • Other Slash Commands
        • Context Variables
        • Generating Commit Messages
        • Using GH CLI
      • Privacy Concerns and Data Usage
    • The Landscape of AI Coding Assistants
      • The Most Popular AI Coding Assistants
        • Devin: The First AI Software Engineer?
        • Devika: An Agentic AI Software Engineer
        • Tabnine: AI-Powered Code Completion
        • DeepCode AI: Security-Focused AI Tool
        • Amazon CodeGuru: AI-Enhanced Code Quality
        • Fauxpilot: An Open-Source Copilot Alternative
        • Privy: A Self-Hosted AI Coding Assistant
        • Aider: A Command-Line Pair Programming Tool
        • SWE-Agent: An Agent-Based Bug Resolution Tool
        • Codeium: An Advanced AI Coding Companion
        • Tabby: Self-Hosted AI Coding Assistant
        • CodeGeeX: An Open-Source Code Generation Model
        • Amazon CodeWhisperer: Real-Time Coding and QA Assistant
        • AskCodi: An AI Code Assistant with a Privacy Focus
        • Blackbox AI: AI-Powered Code Autocomplete and Code Chat
        • Bito: A Deep Code Understanding AI Assistant
      • A Comparative Overview
        • Classification by Type
        • Key Features
        • Open Source vs. Commercial
        • Integration
        • Hardware Requirements
        • Pricing
      • The Rise of Software Agenteers
    • Building Advanced AI Assistants: The Toolkit for the Augmented Developer
      • OpenAI Chat API
      • OpenAI Assistants API
      • LangChain, RAG, Chroma, and Embeddings
      • Good Documentation Can Enhance Developer Productivity, but a Smart Assistant Can Do More
      • No-Code Assistants
        • ChatGPT’s GPTs: Custom Plugins to Extend ChatGPT’s Capabilities
        • Dify: Open-Source Platform for Developing LLM Applications
        • FlowiseAI: Drag & Drop UI for LLM Flows Creation
        • Langflow: A Dynamic Graph-Based Low-Code Platform for AI Applications
        • LLMStack: No-Code LLM Agents and Workflows
      • An Overview of AI Assistant Solutions
        • Programming Approach
        • Supported LLMs
        • RAG Engine
        • Workflow and Orchestration
        • Agent-Based Approach
        • Observability
        • Enterprise Features: SSO/Access Control
        • Local Deployment
    • Developer Productivity and Prompt Engineering
      • The Importance of Prompt Engineering for Productivity
      • Zero-Shot Learning
      • Few-Shot Learning
      • Chain of Thought (CoT)
      • Self-Consistency
      • Tree of Thought Prompting and Autonomous Agents
      • General Knowledge Prompting
      • Best Practices for Prompt Engineering
        • Use Action Verbs
        • Provide Clear Context
        • Use Role-Playing
        • Break Down Complex or Lengthy Tasks
        • Styles, Tones, Formats, Structures, Lengths
        • Use Single Quotes, Double Quotes, and Triple Quotes
      • Using Separators
      • Providing Examples and Guiding the Model
        • Refining Continuously
        • Viewing the Problem from Different Angles
        • Stay Curious, Experiment, and Never Stop Learning
    • Ethical, Social, and Legal Implications of AI in Software Development
      • The Controversy Surrounding GitHub Copilot
      • Can We Really Trust AI to Write Code?
      • The Impact of AI-Generated Code on Security
      • Will AI Replace Developers?
    • Afterword

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