Building Generative AI for Enterprise A practical guide to developing GenAI systems to solve business problems

Generative AI is transforming the corporate world, and understanding its core architecture is now essential for every tech professional. This book serves as a complete roadmap, guiding you from the theoretical foundations of transformer models to the successful deployment of production-ready AI systems.
This book systematically covers the AI lifecycle, moving from transformer foundations and SOTA models like GPT-4o to advanced prompt engineering and fine-tuning with LoRA on Google Colab. You will progress through building RAG systems and Knowledge Graphs with LangChain, eventually developing autonomous agents using ReAct and LlamaIndex. Finally, the book details deploying scalable systems via FastAPI and Kubernetes while ensuring security through AI governance and Microsoft's Responsible AI Toolbox.
By the end of this book, you will have built real-world projects in healthcare and finance. This book equips practitioners with the skills and confidence needed to build, deploy, and enhance generative AI systems that are enterprise-ready and future-proof.
What you will learn
● Build end-to-end generative AI systems for enterprises.
● Apply practical approaches to real-world AI projects.
● Develop a strong understanding of AI models.
● Master in-demand skills like advanced prompt engineering.
● Implement retrieval-augmented generation for business data.
● Solve concrete business problems using AI techniques.
Who this book is for
This book is for IT professionals, students, and enterprise data science teams aspiring to build AI systems. It requires basic Python programming knowledge, but no prior experience with machine learning is necessary, making it ideal for software engineers and aspiring architects.
Table of Contents
1. Introduction to Transformer Models
2. The SOTA GenAI Models
3. GenAI in Industry
4. Prompt Engineering
5. Fine-tuning Generative Models
6. Enhancing GenAI with RAG
7. Advanced RAG in Action
8. Agentic Workflows
9. Deploying GenAI Systems
10. AI Governance and Ethics
11. Future of Generative AI

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