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Generative Software Engineering by Ajit Singh
English | December 3, 2025 | ISBN: N/A | ASIN: B0G4XTWF9K | 374 pages | EPUB | 2.62 Mb
"Generative Software Engineering" is a comprehensive textbook and practical guide designed for the new era of software development. It systematically demystifies the process of building, deploying, and maintaining software systems powered by generative artificial intelligence. This book serves as a bridge between the abstract concepts of AI and the concrete discipline of software engineering, providing a unified methodology for creating "Software 3.0."
Philosophy
The core philosophy of this book is that generative AI is not merely a new feature to be added to existing software, but a fundamental paradigm shift that redefines the very nature of software itself. We move from a world of deterministic logic, where every instruction is explicitly coded, to a world of probabilistic systems, where behavior is learned from data. The software development process, therefore, transforms from writing code to a more holistic practice of curating datasets, designing model architectures, engineering prompts, fine-tuning pre-trained models, and implementing robust MLOps pipelines. This book champions an engineering-first approach, emphasizing reliability, scalability, testability, and maintainability in the context of generative systems.
Key Features
1. Globally Relevant Syllabus: The content is designed to be universally applicable, fitting seamlessly into the B.Tech and M.Tech Computer Science syllabi of international universities.
2. End-to-End Coverage: The book covers the complete lifecycle of generative software, from ideation and data engineering to model implementation, deployment, monitoring, and ethical governance.
3. Simplest Possible Explanations: Complex topics like Transformer architectures, MLOps pipelines, and vector databases are broken down into simple, easy-to-understand components.
4. Code-First Examples: All examples are provided with working code snippets (primarily in Python) using popular frameworks like PyTorch, TensorFlow, and libraries from the Hugging Face ecosystem.
5. Focus on Engineering Discipline: Unlike purely theoretical AI books, this text emphasizes software engineering principles such as testing, versioning (for data and models), quality assurance, and deployment strategies tailored for AI systems.
To Whom This Book Is For
This book is intended for a broad audience within the technology landscape:
1. Undergraduate (B.Tech/B.E.) Students: Computer Science and Information Technology students who want a foundational and practical understanding of building modern AI applications.
2. Postgraduate (M.Tech/M.S.) Students: Students specializing in AI, Machine Learning, or Software Engineering who need a structured text that connects these domains.
3. Software Engineers & Developers: Professionals looking to upskill and transition from traditional software development to building generative AI-powered products.
4. AI/ML Practitioners: Data scientists and machine learning engineers who want to understand the software engineering lifecycle for deploying their models into robust, production-grade applications.
5. Academics & Researchers: Educators and researchers seeking a comprehensive textbook for courses on AI-driven software development or applied AI.
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