Generative AI with Agentic AI Programming: A Practical Guide to Generative AI, AI Agents, LLMs, LangChain, LangGraph, RAG, and Intelligent Automation

The book begins by introducing the foundations of Artificial Intelligence, Machine Learning, Deep Learning, and Large Language Models (LLMs). Readers will explore how Generative AI creates text, images, code, audio, and other forms of content using transformer-based architectures and foundation models. It also explains prompt engineering techniques that help maximize the capabilities of modern AI models.

Moving beyond content generation, the book explores Agentic AI—AI systems that can think, plan, use tools, maintain memory, retrieve information, and complete complex workflows with minimal human intervention. Readers will learn how AI agents interact with external systems, APIs, databases, and knowledge sources to solve real-world problems.

The book provides hands-on coverage of Retrieval-Augmented Generation (RAG), Vector Databases, Embeddings, Knowledge Graphs, Multi-Agent Systems, AI Memory, Function Calling, Tool Integration, Workflow Automation, and AI Orchestration Frameworks. It also demonstrates how to build AI-powered chatbots, virtual assistants, autonomous agents, and enterprise AI applications using popular Python libraries and frameworks.

In addition to implementation, this book addresses the ethical and practical aspects of Generative AI, including AI safety, bias, hallucinations, data privacy, responsible AI development, model evaluation, and deployment strategies. Readers will gain a solid understanding of how to design secure, reliable, and scalable AI solutions for modern applications.

Key Features

Comprehensive introduction to Generative AI and Agentic AI

Fundamentals of AI, Machine Learning, Deep Learning, and Large Language Models

Prompt Engineering techniques for effective AI interactions

Retrieval-Augmented Generation (RAG) and Vector Databases

Embeddings, Semantic Search, and Knowledge Retrieval

AI Agents, Autonomous Workflows, and Multi-Agent Systems

Tool Calling, Function Calling, API Integration, and Memory Management

AI Frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, and LangGraph

Building intelligent chatbots, assistants, and enterprise AI applications

AI ethics, security, responsible AI, and deployment best practices

Practical examples, real-world projects, interview questions, and coding exercises