Generative AI is a practical and comprehensive guide to understanding and building modern AI applications powered by Large Language Models (LLMs) and generative AI technologies.

The book starts with the fundamentals of Artificial Intelligence and Generative AI and gradually progresses to advanced concepts such as Large Language Models, prompt engineering, embeddings, vector databases, Retrieval-Augmented Generation (RAG), LangChain, AI agents, tool calling, multimodal AI, and Agentic AI.

Readers will learn how to work with modern AI models, design effective prompts, build intelligent chatbots, process documents, create question-answering systems, integrate external tools and APIs, and develop real-world AI applications using Python and popular AI frameworks.

The book emphasizes practical implementation and real-world use cases, making it suitable for learners who want to move beyond AI theory and build useful Generative AI solutions.

What You Will Learn
Introduction to Artificial Intelligence and Generative AI
Generative AI Architecture and Applications
Large Language Models (LLMs)
Tokens, Context Windows, and Model Parameters
Popular Generative AI Models and APIs
Prompt Engineering Fundamentals
Zero-Shot, One-Shot, and Few-Shot Prompting
Role-Based and Structured Prompting
Prompt Templates and Advanced Prompt Techniques
Text Generation and Content Creation
Text Summarization and Question Answering
AI Chatbots and Conversational Applications
Embeddings and Semantic Representations
Vector Databases and Similarity Search
Retrieval-Augmented Generation (RAG)
Document Loading and Text Processing
Text Splitting and Chunking Strategies
Semantic Search and Information Retrieval
Building Document Question-Answering Systems
LangChain Fundamentals
LLM and Chat Model Integration
Prompt Templates and Chains
LangChain Runnables and Workflows
Agents and Tool Calling
Function Calling and External API Integration
AI Memory and Conversational Applications
LangGraph and Agentic AI Workflows
Multi-Step AI Workflows
Human-in-the-Loop AI Systems
Multi-Agent Systems
Multimodal Generative AI
Text, Image, Audio, and Document-Based AI Applications
Hugging Face and Open-Source AI Models
Python for Generative AI Development
AI Application Development with APIs
Model Evaluation and Response Quality
Error Handling and AI Application Debugging
AI Security and Responsible AI
Performance and Cost Optimization
Deploying Generative AI Applications
Real-World Generative AI Projects