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.