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.
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: