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Architecting AI Software Systems

Architecting AI Software Systems

By : Richard D Avila, Imran Ahmad
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Architecting AI Software Systems

Architecting AI Software Systems

5 (2)
By: Richard D Avila, Imran Ahmad

Overview of this book

Architecting AI Software Systems provides a definitive guide to building AI-enabled systems, emphasizing the balance between AI’s capabilities and traditional software architecture principles. As AI technologies gain widespread acceptance and are increasingly expected in future applications, this book provides architects and developers with the essential knowledge to stay competitive. It introduces a structured approach to mastering the complexities of AI integration, covering key architectural concepts and processes critical to building scalable and robust AI systems while minimizing development and maintenance risks. The book guides readers on a progressive journey, using real-world examples and hands-on exercises to deepen comprehension. It also includes the architecture of a fictional AI-enabled system as a learning tool. You will engage with exercises designed to reinforce your understanding and apply practical insights, leading to the development of key architectural products that support AI systems. This is an essential resource for architects seeking to mitigate risks and master the complexities of AI-enabled system development. By the end of the book, readers will be equipped with patterns, strategies and concepts necessary to architect AI-enabled systems across various domains.
Table of Contents (14 chapters)
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1
Architecting Fundamentals
5
Architecting AI Systems
12
Other Books You May Enjoy
13
Index

Summary

This case study demonstrates how generative AI architecture for knowledge management can transform enterprise information systems. By integrating advanced LLMs with structured retrieval mechanisms, organizations can centralize scattered knowledge into unified, accessible frameworks [3][6][7].

TechSolve’s implementation illustrates how thoughtfully designed AI architectures featuring layered components, robust data pipelines, and feedback-driven improvement mechanisms can improve support operations and enhance customer satisfaction [1].

The key insight: grounding AI responses in verified organizational knowledge through retrieval augmentation, maintaining clear data provenance, and establishing continuous feedback loops [3][5][2].

These architectural patterns extend beyond knowledge management to a wide range of enterprise applications, providing a blueprint for AI integration that balances innovation with governance and scalability with reliability [1].

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Architecting AI Software Systems
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