Engineering Generative AI-Based Software - Softcover

Staroń Ph.D., Miroslaw

Softcover

ISBN 10: 0443276064 ISBN 13: 9780443276064

Publisher: Morgan Kaufmann, 2026

Synopsis

Engineering Generative-AI Based Software discusses both the process of developing this kind of AI-based software and its architectures, combining theory with practice. Sections review the most relevant models and technologies, detail software engineering practices for such systems, e.g., eliciting functional and non-functional requirements specific to generative AI, explore various architectural styles and tactics for such systems, including different programming platforms, and show how to create robust licensing models. Finally, readers learn how to manage data, both during training and when generating new data, and how to use generated data and user feedback to constantly evolve generative AI-based software.

As generative AI software is gaining popularity thanks to such models as GPT-4 or Llama, this is a welcomed resource on the topics explored. With these systems becoming increasingly important, Software Engineering Professionals will need to know how to overcome challenges in incorporating GAI into the products and programs they develop.

• Provides a holistic software engineering approach to constructing generative AI software
• Shows how to use modern models to create long-term products that bring value
• Presents a combination of process, product, and business models for generative AI-based software engineering

About this title

About the Author

Miroslaw Staron is a professor of software engineering at the Department of Computer Science and Engineering at the University of Gothenburg, Sweden. Dr. Staron has been active in national bodies such as AI Sweden, AI Competence for Sweden, and Swedsoft. His research work focuses on software design, metrics, machine learning, and software quality.

From the Back Cover

Software Engineering professionals now face challenges in incorporating GAI into the products and programs they are developing. At this point, the knowledge about developing AI-based software is mostly based on classical AI, i.e., nongenerative ML systems. Developers know how to use machine learning and, to some extent, how to include it in production systems. Engineering Generative-AI Based Software takes software development to the next level by using generative AI instead. Readers learn how to use text and image models as part of larger software systems. The book discusses both the process of developing such software and the architectures for this kind of software, combining theory with practice. Generative AI software is gaining popularity thanks to such models as GPT-4, Llama, and DeepSeek. More and more products use them as part of their feature portfolio, but this software is often limited to web applications or recommendation systems. Author Miroslaw Staron shows readers how to tackle the challenges of professionally engineering generative AI-based systems. The book starts by reviewing the most relevant models and technologies in this area, both theoretically and practically. Once readers know the technologies, the book goes into details of software engineering practices for such systems, e.g., eliciting functional and nonfunctional requirements specific to generative AI, various architectural styles and tactics for such systems, and different programming platforms. The book also shows how to create robust licensing models and the technology to support them. Finally, readers learn how to manage data, both during the training and also when generating new data, as well as how to use the generated data and user feedback to constantly evolve generative AI-based software.

Edition details

• Publisher: Morgan Kaufmann
• Publication date: 2026
• Language: English
• ISBN 10: 0443276064
• ISBN 13: 9780443276064
• Binding: Paperback
• Edition number: 1
• Number of pages: 206