Full Description
This book is a hands-on guide designed to help readers understand, build, and deploy powerful AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic systems, and intelligent chatbots.
Starting with the fundamentals—LLM architecture, tokenization, APIs, and fine-tuning—the book gradually builds toward complex, integrated systems. Readers will learn to implement RAG pipelines using vector databases like FAISS and Pinecone, develop autonomous AI agents that complete multi-step tasks, and create real-world chatbots that understand and adapt to user needs. The approach is project-driven: each chapter includes visual explanations, step-by-step code walkthroughs, and deployment-ready examples. From building a personal assistant that searches your notes to creating a scheduling agent, every project reinforces both technical skills and applied understanding. It emphasizes clarity, inclusivity, and real-world relevance—helping readers move confidently from basic understanding to complex applications.
Whether you're exploring Agentic AI or looking to build production-ready systems, this book gives you the tools to turn curiosity into capability—and innovation into impact.
What you will learn:
Build intelligent chatbots and tools using open-source LLMs like GPT, LLaMA, and Mistral with guided deployment steps.
Combine LLMs with vector databases like FAISS and Pinecone to create accurate, context-aware AI systems.
Design AI agents capable of planning and executing complex workflows for automation and decision-making.
Apply prompt engineering, memory, and multimodal tools to build real-world AI apps for your project portfolio.
Who this book is for:
Machine Learning engineers, data scientists, and AI professionals interested in learning how to build real-world AI systems using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and intelligent chatbots.
Contents
Part I: Foundations of LLMs.- Chapter 1 - Introduction - Large Language Models.- Chapter 2 - Inside the Transformer: Core Architectures.- Chapter 3 - Fine-Tuning and Alignment.- Chapter 4 - Working with LLM APIs.- Part II: Building Intelligent Applications.- Chapter 5 - Designing Your First AI Chatbot.- Chapter 6 - Retrieval-Augmented Generation (RAG).- Chapter 7 - RAG in Action: Personal Knowledge Search.- Chapter 8 - Agentic AI: Beyond Chatbots.- Chapter 9 - Project: Building an Autonomous AI Agent.- Part III: Scaling and Deploying LLM Applications.- Chapter 10 - Model Serving and Inference Optimization.- Chapter 11 - Cloud, Edge, and Hybrid Deployments.- Chapter 12 - Monitoring and Observability.- Part IV: Ethics, Governance, and the Future.- Chapter 13 - Responsible AI and Safety.- Chapter 14 - Governance, Compliance, and Security.- Chapter 15 - The Future of LLMs and Agentic AI.
