Generative AI with Agentic AI by Aravind choudhary

Generative AI with Agentic AI is a comprehensive and practical guide to understanding and building modern AI applications powered by Large Language Models and intelligent AI agents. The book begins with the fundamentals of Generative AI and Large Language Models (LLMs), explaining tokens, context, model parameters, prompt engineering, text generation, embeddings, and modern Transformer-based models. Readers will learn how to design effective prompts and build practical applications using LLMs. The book then explores Retrieval-Augmented Generation (RAG), document processing, text chunking, embeddings, vector databases, semantic search, and retrieval systems for building AI applications that can work with external knowledge. The second part focuses on Agentic AI, explaining how AI agents can reason, plan, make decisions, use tools, maintain memory, interact with external systems, and complete multi-step tasks. Readers will learn LangChain and LangGraph, tool and function calling, agent workflows, state management, human-in-the-loop systems, and multi-agent architectures. With practical examples, Python-based implementations, real-world use cases, and projects, this book provides a complete learning path from Generative AI to Agentic AI and autonomous intelligent applications. What You Will Learn Artificial Intelligence and Generative AI Fundamentals Generative AI Architecture Large Language Models (LLMs) Transformer Architecture Tokens and Context Windows Model Parameters and Model Behavior Text Generation and Chat-Based AI Prompt Engineering Prompt Structure and Design Zero-Shot, One-Shot, and Few-Shot Prompting Role-Based and Context-Aware Prompting Structured Output Prompt Templates Embeddings Semantic Search Vector Databases Retrieval-Augmented Generation (RAG) Document Loading and Processing Text Chunking and Splitting Retrievers and Vector Search RAG-Based Question Answering LangChain LLM and Chat Model Integration Prompt Templates and Chains Runnables and Workflows Document Loaders and Text Splitters Vector Stores and Retrievers LangChain Agents Tools and Function Calling Memory in AI Applications LangGraph Graph-Based AI Workflows Nodes and Edges State Management Conditional Routing Loops and Iterative Workflows ToolNode and Tool Integration Human-in-the-Loop Systems Agentic AI Fundamentals Generative AI vs Agentic AI AI Agent Architecture Reasoning and Decision Making Planning and Task Decomposition Agent Memory Tool Usage and External APIs Database and Web Tool Integration Autonomous AI Workflows Multi-Agent Systems Agent Collaboration Supervisor and Worker Agent Architecture Agent Communication Error Handling and Recovery Agent Evaluation and Monitoring AI Security Prompt Injection Protection Tool Permissions and Access Control Hallucination Reduction Responsible AI Hugging Face and LLM APIs Multimodal Generative AI AI Chatbots Research Agents Data Analysis Agents Document Analysis Agents Coding Agents Automation Agents Customer Support Agents AI-Powered Business Workflows Deployment of AI Applications Real-World Generative AI Projects Real-World Agentic AI Projects