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Covers transforming requirements into clear specifications, using AI coding agents within controlled workflows, generating and improving tests, and applying code review and refactoring practices. Includes security-first guidance, reliability and error handling, deployment planning, and monitoring to manage spec drift, supported by practical projects and tool recommendations.","Spec-Driven AI Engineering  \nBuild Reliable Software from Requirements to Code with AI Agents, Tests, and Production Workflows  \nBy Gem Iroko  \nA practical, self-contained guide to turning ideas into reliable software with specifications, AI agents, tests, reviews, deployment, and monitoring.  \n[OceanofPDF.com](OceanofPDF.com)  \nCopyright (c) 2026 Gem Iroko. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means without prior written permission from the publisher or author, except for brief quotations used in  \nreviews or educational commentary.  \nThis book is written for educational and professional learning purposes. The examples, workflows, templates, and code-like structures are provided as practical guidance, not as a guarantee of fitness for any particular  \nproduction system.  \nTechnology changes quickly. Before you use any tool, command, package, platform, or workflow in a live environment, review the current documentation for that tool and test the workflow in a safe environment  \nfirst.  \nAll product names, trademarks, and registered trademarks mentioned in this book belong to their respective owners. Their use here is for identification  \nand educational explanation only.  \nFirst Edition.  \nPublisher: Gem Iroko  \nPrinted edition prepared for black-and-white paperback readability.  \n[OceanofPDF.com](OceanofPDF.com)  \nDedication  \nTo every builder who wants AI to make software engineering clearer, safer, and more reliable-not more chaotic.  \nMay this book help you turn vague ideas into well-specified systems, and may every project you build become easier to understand, test, review, deploy, and maintain.  \n[OceanofPDF.com](OceanofPDF.com)  \nAcknowledgments  \nThis book exists because software work is changing. Product managers are writing more technical requests. Developers are reviewing more AIgenerated code. Teams are learning that speed without structure can create hidden risk. Spec-driven engineering is one practical answer to that challenge.  \nI am grateful to the engineers, product thinkers, testers, security reviewers, technical writers, and AI practitioners whose daily work shows that good software begins long before implementation.  \nSpecial appreciation goes to every reader who approaches AI-assisted development with discipline. When you choose clear requirements, measurable acceptance criteria, traceable tasks, and reviewable changes, you help make AI-assisted engineering more professional and dependable. Finally, thank you to the builders who are not satisfied with shallow prompting. This book is for you: the reader who wants to understand the system, control the workflow, and build software that can survive real use.  \n[OceanofPDF.com](OceanofPDF.com)  \nTable of Contents  \nDedication  \nAcknowledgments  \nPreface  \nHow to Use This Book  \nWho This Book Is For  \nWhat You Will Build Throughout This Book  \nRecommended Tools and Development Environment  \nSuggested Project Workspace  \nPART I  \nFoundations of Spec-Driven AI Engineering  \nChapter 1  \nWhy Spec-Driven AI Engineering Matters  \nChapter 2  \nFrom Ideas to Engineering Intent  \nChapter 3  \nThe Spec-Driven AI Engineering Lifecycle  \nChapter 4  \nSetting Up the Spec-Driven AI Engineering Workspace  \nPart II  \nRequirements, Specifications, and System Design  \nChapter 5  \nWriting Clear Software Requirements  \nChapter 6  \nBuilding a Product Requirements Document  \nChapter 7  \nCreating Technical Specifications  \nChapter 8  \nDesigning Software Architecture with AI Support  \nChapter 9  \nData Models, APIs, and Integration Specs  \nChapter 10  \nRequirements Traceability from Spec to Code  \nPart III  \nAI Agents, Prompts, and Controlled Spec-to-Code Workflows Chapter 11  \nWorking with AI Coding Agents  \nChapter 12  \nContext Engineering for AI Development  \nChapter 13  \nPrompting from Specifications  \nChapter 14  \nTurning Specifications into Engineering Tasks  \nChapter 15  \nGitHub and Ve","cbCaidqeued2IXPV","https://ap.wps.com/l/cbCaidqeued2IXPV","pdf",19102784,1,408,"English","en",105,"# Preface\n## How to Use This Book\n## Who This Book Is For\n## What You Will Build Throughout This Book\n## Recommended Tools and Development Environment\n## Suggested Project Workspace\n# Part I - Foundations of Spec-Driven AI Engineering\n## Chapter 1 - Why Spec-Driven AI Engineering Matters\n## Chapter 2 - From Ideas to Engineering Intent\n## Chapter 3 - The Spec-Driven AI Engineering Lifecycle\n## Chapter 4 - Setting Up the Spec-Driven AI Engineering Workspace\n# Part II - Requirements, Specifications, and System Design\n## Chapter 5 - Writing Clear Software Requirements\n## Chapter 6 - Building a Product Requirements Document\n## Chapter 7 - Creating Technical Specifications\n## Chapter 8 - Designing Software Architecture with AI Support\n## Chapter 9 - Data Models, APIs, and Integration Specs\n## Chapter 10 - Requirements Traceability from Spec to Code\n# Part III - AI Agents, Prompts, and Controlled Spec-to-Code Workflows\n## Chapter 11 - Working with AI Coding Agents\n## Chapter 12 - Context Engineering for AI Development\n## Chapter 13 - Prompting from Specifications\n## Chapter 14 - Turning Specifications into Engineering Tasks\n## Chapter 15 - GitHub and Version Control for AI Agent Workflows\n## Chapter 16 - Building the Spec-to-Code Pipeline\n# Part IV - Test-Driven and Quality-Driven AI Engineering\n## Chapter 17 - Test Planning Before Code Generation\n## Chapter 18 - Using AI to Generate and Improve Tests\n## Chapter 19 - Debugging AI-Generated Software\n## Chapter 20 - Code Review and Refactoring with AI Agents\n# Part V - Security, Reliability, and Production Workflows\n## Chapter 21 - Security-First Spec-Driven Engineering\n## Chapter 22 - Reliability and Error Handling\n## Chapter 23 - Deployment Planning and DevOps Workflows\n## Chapter 24 - Monitoring, Maintenance, and Spec Drift\n# Part VI - Real-World Spec-Driven AI Engineering Projects\n## Chapter 25 - Project 1-Building a Task Management App from Requirements to Tested Code\n## Chapter 26 - Project 2—Building an AI Customer Support System from Specs to Deployment\n## Chapter 27 - Project 3-Building a SaaS Analytics Dashboard with a Spec-First Workflow\n## Chapter 28 - Capstone Project-Building a Production-Ready AI-Powered Web Application\n# Part VII - Scaling Spec-Driven AI Engineering\n## Chapter 29 - Working with Product Managers, Developers, and AI Agents\n## Chapter 30 - Building a Repeatable Spec-Driven AI Engineering System\n# A Small Request from the Author\n# Appendices","[{\"question\":\"How does spec-driven AI engineering improve reliability compared to prompt-only coding?\",\"answer\":\"It starts from intent, requirements, specifications, tasks, tests, and review rules, so the AI agent builds within an engineering system rather than relying on a vague prompt. This reduces risks like wrong features, missed security rules, and unnoticed edge cases.\"},{\"question\":\"What workflow connects specifications to generated code?\",\"answer\":\"The book describes turning specifications into engineering tasks, using context engineering and prompting from specs, and building a spec-to-code pipeline with version control. It emphasizes traceability from spec to code so changes remain reviewable.\"},{\"question\":\"How are tests and quality ensured when AI generates code?\",\"answer\":\"It covers test planning before code generation, using AI to generate and improve tests, and debugging AI-generated software. Code review and refactoring with AI agents support quality and maintainability.\"},{\"question\":\"What practices help maintain security and production readiness over time?\",\"answer\":\"Security-first guidance, reliability and error handling, and deployment planning are paired with monitoring and maintenance. 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This reduces risks like wrong features, missed security rules, and unnoticed edge cases.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What workflow connects specifications to generated code?",{"text":79,"@type":75},"The book describes turning specifications into engineering tasks, using context engineering and prompting from specs, and building a spec-to-code pipeline with version control. It emphasizes traceability from spec to code so changes remain reviewable.",{"name":81,"@type":72,"acceptedAnswer":82},"How are tests and quality ensured when AI generates code?",{"text":83,"@type":75},"It covers test planning before code generation, using AI to generate and improve tests, and debugging AI-generated software. 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