Engineering Generative Ai-Based Software

Erscheinungsjahr 2026
ISBN: 978-0-443-27606-4
Verlag: Elsevier Science

Buch, Englisch, 206 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 449 g

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.

Autoren/Hrsg.

Staro&

Staron, Miroslaw

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering

Inhaltsverzeichnis

1. Introduction
2. Generative AI basics
3. Constructing Generative AI software
4. Functional and Non-Functional Requirements for Generative AI Software
5. Architecting Generative AI Software
6. Implementation and Quality Assurance of Generative AI Software
7. Handling Data for Generative AI Systems
8. Deployment of Generative AI Software
9. Generative AI Ecosystems
10. Summary and Current Trends

Über Autor(innen)

Staron, Miroslaw
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.