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Architecting AI Solutions: A Blueprint for Generative AI

In: AI and the Boardroom

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  • Rohan Sharma

Abstract

How do you build an AI solution that not only works but thrives in a complex business environment? The Generative AI Reference Architecture offers a structured blueprint, guiding enterprises from data preparation to deployment and monitoring. It ensures AI systems are secure, scalable, and impactful, capable of delivering real business value. Starting with an emphasis on user experience, the architecture integrates intuitive interfaces, effective prompt engineering, and retrieval augmentation to optimize AI outputs. It also includes adaptation and tuning for performance, MLOps orchestration for lifecycle management, and stringent security, privacy, and compliance measures to protect AI models. This framework further focuses on governance, responsible AI practices, and seamless enterprise integration, ensuring AI systems are both effective and trustworthy. Key takeaway: Implementing the Generative AI Reference Architecture is about creating a robust, user-focused AI system that is secure and adaptable. Are your AI initiatives ready to leverage such a well-defined architecture to drive real business growth?

Suggested Citation

  • Rohan Sharma, 2024. "Architecting AI Solutions: A Blueprint for Generative AI," Springer Books, in: AI and the Boardroom, chapter 0, pages 259-273, Springer.
    • Handle: RePEc:spr:sprchp:979-8-8688-0796-1_21
    DOI: 10.1007/979-8-8688-0796-1_21
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