Architecting Enterprise AI: A Practical Guide to RAG, Knowledge Architecture, Governance, Security, and Production AI
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Book Overview
For Architecting Enterprise AI, I would position the metadata around enterprise AI architecture + RAG + governance, rather than making it sound like another general generative-AI book.Book Description - copy/pasteEnterprise AI succeeds or fails long before a model generates its first response.
Architecting Enterprise AI is a practical guide for architects, engineers, technology leaders, and AI practitioners responsible for turning promising AI ideas into secure, governed, production-ready systems.
Rather than focusing only on models and prompts, the book examines the architectural decisions that determine whether enterprise AI can actually work at scale. Readers learn how to frame AI problems, choose the right AI approach, design enterprise AI architectures, organize and secure knowledge, work effectively with large language models, and build Retrieval-Augmented Generation (RAG) systems grounded in trusted enterprise information.
Through architecture patterns, decision frameworks, practical examples, governance checklists, and real-world scenarios, Manoj Annavajjala shows how identity, knowledge architecture, retrieval, security, governance, reliability, and operational readiness fit together.
For professionals moving from AI experimentation to enterprise implementation, this book provides a practical framework for answering the question that matters most:
How do we design AI systems that organizations can actually trust and operate in production?
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