Description
Coding has always been a story of evolution.
From binary switches to assembly language, from text editors to intelligent code assistants -- every leap didn't just change how we code, it changed how we think about coding.
Today, AI is not just a tool; it's a partner.
AI can suggest functions, generate full classes, design architectures -- sometimes better than junior developers.
But without understanding how AI actually works, developers risk frustration, hidden bugs, tech debt, and fragile systems.
This book is your guide to mastering AI-assisted coding.
You will learn:
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How AI models like LLMs (Large Language Models) actually generate code -- and why they sometimes fail.
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How to structure prompts to control AI behavior, improve quality, and minimize hallucinations.
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How to manage context windows, token limits, floating point precision, and other hidden traps that affect AI outputs.
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How to debug, unit test, and critically review AI-generated code like a senior engineer.
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How to teach AI assistants your internal libraries and standards without needing full model retraining.
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How leaders can design AI-driven coding workflows, set guardrails, and future-proof their organizations.
We will not treat AI as a "black box magic" tool.
We will treat it as a system -- understandable, guideable, and improvable -- just like any other complex engineering tool.
This book is based on real-world experience as a software engineer, an engineering leader, and a deep explorer of AI systems.
It is practical, direct, and focused on helping you think better, code better, and lead better in an AI-accelerated world.
You will not just "use" AI. You will orchestrate it.
If you are a developer who wants to future-proof your skills, or a leader preparing your teams for AI-driven development,
this book will help you bridge the gap between traditional coding and the next era of software building.
Let's begin.
The future is already typing alongside you.
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