Generative AI and agentic AI are reshaping how we interact with data, enabling intelligent systems that can reason, generate, and autonomously act across multiple modalities. From text and images to voice and structured data, these technologies are increasingly essential in enterprise and research applications today.
This book offers a complete roadmap to mastering multimodal generative AI and agentic AI systems. It covers foundational concepts, vision-language models, retrieval-augmented generation, human-in-the-loop and multi-agent workflows, text-to-SQL, OCR, and hybrid AI integrations. Each chapter combines theory, practical guidance, code implementations, and real-world case studies, helping readers understand architectures, pipelines, and production-grade deployments.
By the end of this book, readers will be capable of designing, implementing, and scaling robust multimodal and agentic AI systems. They will gain hands-on expertise in reasoning, generation, retrieval, agent orchestration, and Ops, equipping them to build production-ready AI applications and excel in their roles.
What you will learn
● Understand multimodal generative AI and agentic AI systems.
● Architecting RAG, vector DBs, embeddings, cross-encoders, and core agentic planning.
● Build retrieval-augmented generation workflows efficiently.
● Implement human-in-the-loop and multi-agent pipelines.
● Apply text-to-SQL for real-time data queries.
● Develop OCR solutions for images and documents.
● Integrate traditional ML models with GenAI workflows.
● Deploy production-grade AI with monitoring and observability.
Who this book is for
This book is for AI enthusiasts, data scientists, ML engineers, and software developers with foundational Python and ML knowledge. It is ideal for tech leads, researchers, and enterprise architects aiming to build and scale multimodal, production-grade agentic AI systems.
Table of Contents
1. Introducing New Age Generative AI
2. Deep Dive into Multimodal Systems
3. Implementing Unimodal Local GenAI System
4. Implementing Unimodal API-based GenAI Systems
5. Implementing Agentic GenAI Systems with Human-in-the-loop
6. Two and Multi-stage GenAI Systems
7. Building a Bidirectional Multimodal Retrieval System
8. Building a Multimodal RAG System
9. Building GenAI Systems with Reranking
10. Retrieval Optimization for Multimodal GenAI
11. Building Multimodal GenAI Systems with Voice as Input
12. Advanced Multimodal GenAI Systems
13. Advanced Multimodal GenAI Systems Implementation
14. Building Text-to-SQL Systems
15. Agentic Text-to-SQL Systems and Architecture Decision-Making
16. GenAI for Extracting Text from Images
17. Integrating Traditional AI/ML into GenAI Workflow
18. LLM Operations and GenAI Evaluation Techniques
