Book
Generative AI for Software Development
Building Software Faster and More Effectively
Sergio Pereira
Summary
Sergio Pereira tested as many AI development tools as he could find and wrote this to give practitioners a way to evaluate them instead of chasing whichever tool is loudest that week. The book moves chapter by chapter through where generative AI actually gets used in a development workflow: code generation and autocomplete, UI and UX design, bug detection and code review, automated testing, predictive performance work, documentation, and chat-based assistants, closing with case studies of teams that adopted these tools successfully. It doesn't assume a machine learning background, and each chapter builds toward one question: which tool fits this task, and where does it fall short.
Target Readers
- Developers who already use one AI coding tool and want a framework for evaluating others against their actual workflow
- Engineering managers who need to judge generative AI's real productivity impact rather than take a vendor's claims at face value
- Teams deciding where in their development process, code review, testing, or documentation, AI assistance is worth adopting first
Tags
Colophon
- Publisher
- O'Reilly Japan
- ISBN
- 978-4-8144-0146-8
- Published
- Jan 2026
- List price
- ¥3,080incl. taxMay differ from the actual selling price on Amazon
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Prerequisites
- Recommended
Software Engineering at Google
Lessons Learned from Programming Over Time
Titus Winters, Tom Manshreck, Hyrum Wright
Reason: After understanding the engineering practices of large organizations, you view how generative AI changes the flow of development with the same discipline. AI is a tool that pays off only on a foundation of review, testing, and design judgment.
- Recommended
The Pragmatic Programmer
your journey to mastery, 20th Anniversary Edition
David Thomas, Andrew Hunt
Reason: Once you have the pragmatic mindset—sharpen your tools, own your judgment—you take in generative AI as a new tool. The ability to verify AI output rather than trust it blindly is the very craftsmanship of the AI era.
Next Books
- Recommended
Responsible Software Engineering
With Real-World Case Studies from Google
Daniel Barrett, Google Engineering
Reason: Once you can bring AI into development, you advance to the question of responsibility—the impact that power has on society. You take a view that weaves correctness, bias, and accountability of outputs into design from the start.
- Recommended
Seisei AI Application Hyoka Nyumon (An Introduction to Evaluating Generative AI Applications)
Shinsuke Matsuki
Reason: After learning to use generative AI as a development tool, you face the next question: how do you measure the quality of an application that itself embeds generative AI? This book covers evaluation methods along the development lifecycle, from LLM-as-a-Judge to security evaluation and AI agent evaluation.
- Related
Kaihatsu Koritsu o Up Suru! Claude Code Jitsuyo Nyumon (A Practical Introduction to Claude Code for Boosting Development Efficiency)
Fumitaka Osawa
Reason: After comparing AI tools across the whole development process — code generation, review, test automation, documentation — and gaining criteria for 'which tool, when,' you naturally want to pick one and go deep hands-on. Claude Code Practical Guide builds up, from setup and conversational operation to extension via MCP servers and skills, focused on a single AI coding agent, turning the broad view of tool selection into concrete operational skill you can use in practice.