Generative AI Tutorial

Generative AI focuses on building models that can create new content such as text, images, audio and code by learning patterns from existing data to generate human‑like outputs across various domains. It is widely used in chatbots, content creation, design and automation.

Basics

Understanding the foundations of AI and deep learning is essential for working with GenAI models.

• Introduction to Artificial Intelligence
• Introduction Generative AI
• Neural Networks
• RNNs, LSTMs, GRUs
• Transformers & Self-Attention
• Autoencoders & Latent Space
• GANs & Diffusion Models

Python for Agentic AI

Python is used in Agentic AI for building intelligent agents, automating decision-making workflows and integrating AI models with external tools and APIs.

• Introduction
• Download and Install Python 3
• Python Variables
• Python Data Types
• Python Operators
• Conditional Statements in Python
• Loops in Python
• Python Functions
• Python String
• Python Lists
• Python Dictionary

Tools

To get started with Generative AI, you need to build expertise in the following tools and libraries:

• PyTorch
• TensorFlow
• Hugging Face Transformers
• LangChain
• LangGraph
• Langflow
• LlamaIndex
• Integration of Langchain with Llama-Index

Natural Language Processing Basics

Most Generative AI models are built on NLP concepts.

• Text Preprocessing in NLP
• Bag of Words & TF-IDF
• Word2Vec & GloVe
• Introduction to BERT
• Introduction to GPT Models
• Hugging Face Models

Prompt Engineering

Prompt engineering is the practice of crafting inputs to get better outputs from LLMs.

• What is Prompt Engineering?
• Zero-Shot, One-Shot & Few-Shot Prompting
• Chain of Thought Prompting
• Role & Contextual Prompting
• ReAct (Reasoning + Acting) Prompting
• Retrieval-Augmented Prompting
• Self-Consistency Prompting
• Tree of Thought (ToT) prompting
• Guardrails in AI

Large Language Models (LLMs)

LLMs are the backbone of modern Generative AI systems.

• Large Language Model
• LLM Parameters
• Scaling Laws in LLMs
• Fine-Tuning LLMs with LoRA , QLoRA & PEFT
• RLHF: Reinforcement Learning from Human Feedback
• LLM Distillation
• Popular LLMs: Claude, LLaMA, Gemini
• LLM APIs: OpenAI, Hugging Face, Gemini

Retrieval-Augmented Generation (RAG)

RAG combines LLMs with external knowledge sources for more accurate responses.

• RAG in AI
• RAG Architecture
• Multimodal RAG
• Embeddings
• Vector Databases: FAISS, ChromaDB, Qdrant, Pinecone 
• RAG System with Langchain and Langraph

Agentic AI & Multi-Agent Systems

Agentic AI extends LLMs with autonomy, memory and collaboration.

• Introduction to Agentic AI
• Agent vs Traditional AI
• Agent Architectures & Memory
• Agent-to-Agent Communication
• AI Agent Frameworks
• Model Context Protocol (MCP)

CrewAI and Orchestration

CrewAI is a framework for coordinating multiple AI agents to work collaboratively.

• Introduction to CrewAI
• CrewAI Tools
• Creating Custom Tools for CrewAI
• Memory in CrewAI
• CrewAI Embeddings
• CrewAI Collaboration
• CrewAI Knowledge
• CrewAI Planning and Reasoning
• CrewAI CLI
• CrewAI Flow
• Fraud Detection Using CrewAI Project

Automation with Agents and Deployement

Generative AI can be extended into workflows for business automation.

• Agentic RAG
• Agentic RAG with LlamaIndex
• Introduction to n8n
• Automated Email Classifier with n8n
• AI Deployment with Gradio, Streamlit, FastAPI

Responsible & Ethical AI

Generative and Agentic AI raise ethical challenges that must be addressed.

• Bias in AI Models
• Deepfakes
• Prompt Injection in LLM
• Responsible AI Practices

Projects

This section presents practical, hands-on project ideas to help you apply Generative AI concepts and build a strong portfolio.

• Building an AI application with LlamaIndex
• PDF Summarizer LLM Application
• RAG(Retrieval-Augmented Generation) using LLama3
• Simple Retrieval Augmented Generation using Java
• Building AI Agents with Phidata
• PDF Summarizer using RAG

Careers in Generative AI

Generative AI and Agentic AI are among the fastest-growing career domains in tech. Key job roles include:

• AI Engineer
• Machine Learning Engineer
• Data Scientist
• Prompt Engineer
• Agentic AI Engineer