Overview

Your AI coding tools aren't the problem. How you're using them is. Every day, developers waste hours fighting AI-generated bugs, reviewing questionable code, and wondering if they're actually more productive-or just busier. Teams roll out expensive AI tools company-wide, only to see quality collapse and velocity stall within months. You're not alone. And there's a better way. AI Coding in Practice reveals what actually works when implementing AI coding tools-from solo developers to 500-person engineering teams. Based on patterns from documented real-world implementations across startups, enterprises, and regulated industries, this comprehensive guide eliminates guesswork and prevents million-dollar mistakes. Inside, you'll discover: Professional prompting strategies that produce maintainable code (not just working code) Code review protocols specifically designed for AI-generated code security and quality Team adoption frameworks proven across startups, mid-size companies, and Fortune 500 enterprises Integration workflows that boost productivity 15-40% while maintaining quality standards Common failure patterns and exactly how to avoid them (including detailed case studies) Advanced techniques for multi-agent workflows, custom automation, and maximum leverage Whether you're: exploring AI tools as an individual developer, rolling out GitHub Copilot to your team, or planning enterprise-wide AI adoption-this book provides the complete roadmap from pilot programs through organizational transformation. Stop guessing. Stop wasting time on trial and error. Learn from organizations that succeeded (and those that failed) so you can implement AI coding tools confidently, safely, and profitably. Your competition is already moving faster. This book shows you how to move faster AND better. Includes: 400+ actionable practices, real case studies with ROI data, security checklists, and implementation frameworks.

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