Generative AI for Software Development: Building Software Faster and More Effectively

Generative AI is changing more than the way developers write code. It is entering design, testing, documentation, code review, analytics, and product workflows too. In Generative AI for Software Development, software engineer and CTO Sergio Pereira offers a practical, comparative guide to that expanding toolkit—grounded in tool evaluations, real-world use cases, and attention to limitations.

Instead of asking readers to accept AI hype at face value, Pereira lays out a consistent way to assess tools and decide where they may—and may not—fit into development work. The book reflects the 2025 tool landscape, making its evaluations a dated snapshot while its emphasis on thoughtful comparison remains central.

Evaluate coding assistants with real work in mind 💻

The opening chapter examines code generation and autocompletion, comparing browser-based assistants with tools integrated into development environments. ChatGPT, Gemini, GitHub Copilot, Cursor, and Windsurf appear among the examples. The focus is not simply on what a tool can generate, but on its use cases, strengths, and limitations—and on how to assess products through practical coding challenges.

Follow AI across the software workflow

The book moves beyond code completion into AI-supported UI/UX and frontend development, bug detection and code review, automated testing and quality assurance, predictive analytics, and technical documentation. It also considers chatbots and virtual assistants, with examples including tools such as Uizard,, Codacy, Katalon Studio, Swimm, Botpress, and LangChain.

By covering these areas together, Pereira gives readers a broader view of where generative AI may intersect with a feature’s development lifecycle. Chapters discuss practical applications alongside trade-offs, including the continuing importance of human review and testing.

Learn from implementation examples

The closing chapter turns to implementation stories involving entrepreneur Pieter Levels and Shopify, then considers how changing tools may affect the future of software development. These cases complement the earlier tool comparisons with examples of AI use in individual and enterprise settings.

A field guide for considered adoption

Written for software engineers, product builders, CTOs, and technically curious readers, this guide is designed for people who want to make informed choices about AI-assisted work without needing to be machine-learning specialists. Its most useful habit is a practical one: compare tools against real needs, understand their limits, and keep human judgment in the workflow.