Engineering Generative AI-Based Software

Miroslaw Staron

ISBN 10: 0443276064 / ISBN 13: 9780443276064

Published by Elsevier Science & Technology, Morgan Kaufmann Jan 2026, 2026

About this Item

Neuware - 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.

Seller Inventory # 9780443276064

Bibliographic Details

• Title: Engineering Generative AI-Based Software
• Publisher: Elsevier Science & Technology, Morgan Kaufmann Jan 2026
• Publication Date: 2026
• Language: English
• ISBN 10: 0443276064
• ISBN 13: 9780443276064
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 234W x 190H x 35D millimeters 234 millimeters width by 190 millimeters height by 35 millimeters depth
• Weight: 450 grams

About this title

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 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.