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Vibe Engineering

Building Production-Grade Software with AI-Assisted Development

This book is 100% completeLast updated on 2026-07-17

AI can generate code in seconds, but shipping production-grade software requires far more than fast prompts. Vibe Engineering shows you how to work with AI coding assistants to build systems that are reliable, secure, and maintainable. Through practical examples and proven workflows, you will learn how to turn AI from a code generator into a trusted engineering partner.

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About

About

About the Book

This book teaches you how to build reliable, maintainable, production-grade software using AI coding assistants. You will learn context engineering, spec-driven development, agentic workflows, security practices, testing strategies, and CI/CD integration. Every chapter includes working code examples, configuration files, and step-by-step walkthroughs you can apply immediately. Whether you are a seasoned engineer looking to level up your AI workflow or a motivated beginner wanting to understand the modern development landscape, this book gives you the practical skills to ship quality software at AI velocity.

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About the Author

Steve Publications

Steve is a technology professional with more than 20 years of experience in software development, server infrastructure, cybersecurity, vulnerability research and reverse engineering. Throughout his career, he has designed, secured, analyzed and tested complex software and infrastructure, with a particular focus on understanding how systems fail and how they can be made more secure.

Outside of work, Steve enjoys sharing knowledge with the technology community. He collaborates with researchers, industry experts and technology professionals to write practical books covering software development, cybersecurity, cloud computing, networking, DevOps, artificial intelligence and enterprise technologies. His books focus on practical learning through clear explanations, real-world examples and hands-on exercises. With more than two decades of industry experience, his goal is to help IT professionals, students and technology enthusiasts build useful skills and stay current in a rapidly changing industry.

We believe readers deserve to know how our books are created. Most of our authors are not native English speakers, so we use AI to help translate, proofread manuscripts, fix grammar, improve sentence structure and make technical explanations easier to read. AI is used as an editing tool only. It does not replace the research, technical knowledge or hands-on experience behind our books. Some of our authors also prefer to remain anonymous for privacy or professional reasons. In those cases, we publish their work under a different name. The author's name may be different, but the quality of the content and our review process remain the same.

Every book is written, reviewed and maintained by experienced technology professionals, with contributions from our private technical community of more than 420 engineers and researchers. We spend far more time validating technical accuracy and keeping our content up to date than generating text. We are always interested in working with experienced professionals who have deep expertise in a particular technology or domain. If you would like to publish a book with us or help review an existing manuscript, we'd love to hear from you. Send us a message describing your area of expertise. We are especially interested in niche technologies, specialized skills and emerging topics that are underrepresented in existing technical literature.

If you look through the contents of our books, you'll see practical examples, detailed explanations and material that is regularly updated. Our goal is to publish books that professionals can actually rely on, not low-effort AI-generated content. If you ever feel that one of our books does not meet that standard, Leanpub offers a 60-day money-back guarantee. Feel free to request a refund if you are not satisfied with your purchase.

Contents

Table of Contents

Building Production-Grade Software with AI-Assisted Development

Introduction: The Vibe Engineering Revolution

  1. What Is Vibe Engineering
  2. The Karpathy Moment and Why It Matters
  3. Vibe Coding vs. Agentic Engineering
  4. What This Book Will Teach You

Chapter 1: The AI-Assisted Development Landscape

  1. From Autocomplete to Agentic Coding
  2. The Tool Landscape: IDEs, CLIs, and Platforms
  3. What the Data Says About Productivity
  4. The Code Quality Debate

Chapter 2: Your AI Development Toolkit

  1. IDE-Native Assistants: Cursor and Windsurf
  2. Terminal Agents: Claude Code and OpenAI Codex CLI
  3. Inline Autocomplete: GitHub Copilot and Alternatives
  4. Browser-Based Platforms: Replit, Bolt, and Lovable
  5. Building Your Tool Stack

Chapter 3: Context Engineering

  1. Why Context Is Your Most Valuable Resource
  2. The Anatomy of a Great CLAUDE.md
  3. Token Budgets and Attention Decay
  4. Just-in-Time Retrieval and Progressive Disclosure
  5. Structured Note-Taking and External Memory

Chapter 4: Prompt Strategies for Code Generation

  1. The Spec-First Prompt Pattern
  2. Chain-of-Thought for Architectural Decisions
  3. Few-Shot Examples and Negative Constraints
  4. Iterative Refinement Loops
  5. Model Selection by Task Type

Chapter 5: Spec-Driven Development

  1. From Vibe Coding to Specs
  2. Writing Effective Specifications
  3. API Endpoints
  4. Non-Functional Requirements
  5. Out of Scope (for this iteration)
  6. The Plan-Build-Verify Cycle
  7. Tools That Support Spec-Driven Workflows

Chapter 6: Architecture and Design for AI-Assisted Development

  1. Designing for AI Generation
  2. Modular Monoliths and Bounded Contexts
  3. API-First Architecture
  4. Event-Driven Patterns and Async Workflows
  5. Anti-Patterns to Avoid

Chapter 7: Testing AI-Generated Code

  1. Why AI-Generated Code Needs Different Testing
  2. Test Generation with AI Assistants
  3. Catching Tautological Tests
  4. Fuzz Testing and Property-Based Testing
  5. Integration and End-to-End Coverage
  6. Troubleshooting: Common AI Testing Pitfalls
  7. The Testing Checklist for AI Code

Chapter 8: Debugging Strategies

  1. The Five-Step Debugging Methodology
  2. Common Failure Patterns in AI-Generated Code
  3. Production Debugging with AI Agents
  4. Observability and Distributed Tracing
  5. When to Fall Back to Classical Debugging

Chapter 9: Security and Supply Chain

  1. The Hallucination Attack Surface
  2. Slopsquatting and Supply Chain Risks
  3. Case Study: Slopsquatting Incident Post-Mortem
  4. Injection Vulnerabilities in AI Code
  5. Secrets Management and Credential Safety
  6. Security Scanning in the AI Pipeline

Chapter 10: CI/CD Integration

  1. The AI-Native CI/CD Pipeline
  2. Automated Code Review with AI
  3. AI-Augmented Test Generation in CI
  4. Self-Healing Pipelines
  5. Deployment Strategies for AI-Assisted Teams

Chapter 11: Collaboration and Team Practices

  1. Code Review in the Age of AI
  2. Documentation as a First-Class Artifact
  3. Knowledge Sharing and Context Propagation
  4. Onboarding and Team Standards
  5. Measuring What Matters: DORA Metrics and Beyond

Chapter 12: The Model Context Protocol (MCP)

  1. What Is MCP and Why It Matters
  2. The Client-Server Architecture
  3. Building Custom MCP Servers
  4. Security and Authorization in MCP
  5. The Growing Ecosystem

Chapter 13: Multi-Agent Orchestration

  1. From Single Agent to Multi-Agent Systems
  2. Orchestration Patterns: Planner, Supervisor, Hive Mind
  3. Frameworks: LangGraph, CrewAI, Microsoft Agent Framework
  4. Parallel Execution and Worktree Strategies
  5. Governance and Guardrails

Chapter 14: Real-World Project Walkthrough

  1. Project Brief: A Task Management API
  2. Phase 1: Specification and Planning
  3. Phase 2: Scaffold and Architecture
  4. Phase 3: Implementation with Agents
  5. Phase 4: Testing and Validation
  6. Phase 5: Deployment and Monitoring

Chapter 15: AI-Assisted Development Across Domains

  1. Mobile Development with AI Assistants
  2. Data Engineering Pipelines with AI
  3. Frontend Framework Integration
  4. Infrastructure as Code and DevOps with AI
  5. Cross-Domain AI Workflow Patterns

Chapter 16: Maintenance, Migration, and Evolution

  1. Refactoring AI-Generated Code
  2. Legacy System Migration
  3. Performance Optimization
  4. Managing Technical Debt at Scale
  5. Keeping Your Codebase AI-Friendly

Conclusion: The Future of Vibe Engineering

  1. The Skills That Will Endure
  2. What Comes Next
  3. Your Role in the AI-Native Future

References

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