Generative AI with Agentic AI

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