Generative AI is a practical and comprehensive guide designed for students, developers, AI enthusiasts, and professionals who want to understand and build applications using modern Generative Artificial Intelligence technologies.

The book begins with the fundamentals of Generative AI and gradually progresses to advanced concepts such as Large Language Models (LLMs), Prompt Engineering, embeddings, vector databases, Retrieval-Augmented Generation (RAG), LangChain, AI Agents, and application development.

With a hands-on and project-oriented approach, readers will learn how Generative AI models work, how to interact with LLMs, create effective prompts, connect AI models with external data, build intelligent applications, and develop real-world AI solutions.

What You Will Learn
Introduction to Artificial Intelligence
Introduction to Generative AI
Generative AI vs Traditional AI
Large Language Models (LLMs)
Understanding AI Models and Tokens
OpenAI and Other Generative AI Models
Prompt Engineering
Zero-Shot and Few-Shot Prompting
Advanced Prompting Techniques
Text Generation and Summarization
Question Answering Systems
Embeddings
Vector Databases
Semantic Search
Retrieval-Augmented Generation (RAG)
Document-Based Question Answering
LangChain Fundamentals
LangChain Components and Chains
Working with LLMs and Chat Models
Hugging Face Models
Building AI Applications with Python
AI Agents and Agentic AI
Tools and Tool Calling
Memory in AI Applications
LangGraph Fundamentals
Multi-Step AI Workflows
Human-in-the-Loop AI Systems
AI Application Development
Model Evaluation and Optimization
Generative AI for Real-World Applications
Building Practical Generative AI Projects