The Augmented Developer: Code Smarter, Not Harder

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 chainsand AI assistants.

The guide dives into advanced prompt engineeringtechniques and best practices used to maximize the potential of AI in coding. It also explores the ethicaland technicalimplications of using generative AI in coding. Moreover, it looks into the future impactsof AI in coding and its potential to change the developer role as we know it.

Across the chapters of The Augmented Developer, you will:

• Explore AI-assisted coding’s impact on pair programming and remote work, enhancing the 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 Copilotto 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.
• Master prompt engineering to boost developer productivity with AI tools like GitHub Copilot and ChatGPT: Learn to create effective prompts for optimal outputs including topics like Few-Shot Learning, Chain of Thoughts, Tree of Thoughts, and more.
• Understand the Best Practices of Prompt Engineering: Acknowledge the best practices of prompt engineeringand 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.
• Explore Advanced LLM Orchestration Tools: Dive into advanced LLM orchestration and workflow tools like DiFy, Flowise AI, and LLMStack. Get an overview of top tools for quick LLM-based solution creation.
• 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.

Code snippets used in this book are available for download.