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Data Science & AI LLMs · RAG · Agents ✔ 3 Levels Available

Generative AI
Full Programme

A structured three-level path from Generative AI foundations and productivity tools through intermediate RAG and LangChain builds, to a GenAI Engineer track covering fine-tuning, agents, MCP, LLMOps, and end-to-end deployment. Format: Theory + Hands-on Labs + Assessments + Capstone.

🎯
Format
Theory + Lab/Assignments
🏆
Certificate
Arich Certified
Enrol Now →
Course Code
GENAI
Click a Level to View its Syllabus
L1
GENAI-BEG · Beginner
9 Modules · 60 Hours
›
L2
GENAI-INT · Intermediate
10 Modules · 120 Hours
›
L3
GENAI-ENG · GenAI Engineer
16 Modules · 210 Hours
›
📄 GENAI-BEG — Generative AI — Beginner (60 Hours)
L1
GENAI-BEG
Generative AI — Beginner (60 Hours)
01
Introduction to AI, ML, Deep Learning & Generative AI
▼
Introduction to Artificial Intelligence
Types of AI
Machine Learning Fundamentals
Deep Learning Fundamentals
What is Generative AI?
Evolution of AI
Real-world AI Applications
Responsible AI and Ethics
Lab: Explore ChatGPT and Microsoft Copilot
Lab: Compare AI-generated responses
02
Python Programming Fundamentals
▼
Installing Python & VS Code
Variables and Data Types
Operators
Conditional Statements
Loops
Functions
Collections
File Handling
Exception Handling
OOP Basics
Lab: Calculator Application
Lab: Student Management Program
03
Prompt Engineering Basics
▼
Introduction to Prompt Engineering
Prompt Structure
Zero-shot
One-shot
Few-shot
Role-based Prompting
Prompt Optimization
Best Practices
Lab: Email Generation
Lab: Content Creation
Lab: SQL Query Generation
04
Introduction to LLMs (GPT-4o, Claude, Gemini)
▼
What are LLMs?
GPT-4o
Claude
Gemini
Llama
Mistral
Model Comparison
Business Use Cases
Lab: Compare outputs from GPT-4o, Claude and Gemini
05
ChatGPT for Productivity
▼
Email Writing
Resume Building
Meeting Summarization
Research Assistance
Coding Assistance
Lab: Create resumes
Lab: Meeting summaries
Lab: Professional emails
06
AI Tools for Daily Work
▼
Microsoft Copilot
Google Gemini
Claude AI
Perplexity AI
Canva AI
Gamma AI
NotebookLM
Lab: Create presentations
Lab: Summarize PDFs
Lab: Generate reports
07
Basic OpenAI API
▼
Introduction to APIs
OpenAI Platform
API Keys
Authentication
Chat Completions API
Python Integration
Tokens
Best Practices
Lab: Generate API Key
Lab: Build AI Chatbot
08
Mini AI Project
▼
AI Resume Generator
AI Email Assistant
AI Content Generator
AI FAQ Chatbot
AI Travel Planner
Project Documentation
Project Presentation
Lab: Develop and present a mini AI application
09
Assessment & Certification
▼
Module Quizzes
Python Programming Assessment
Prompt Engineering Exercises
AI Tool Assignments
Mini Capstone Project
Viva
Final Certification Assessment
📄 GENAI-INT — Generative AI — Intermediate (120 Hours)
L2
GENAI-INT
Generative AI — Intermediate (120 Hours)
01
Advanced Python for AI
▼
Advanced Python
NumPy
Pandas
Matplotlib
REST APIs
Async Programming
Virtual Environments
Project Structure
Lab: Data analysis
Lab: REST API integration
02
Transformer Architecture
▼
NLP Evolution
Self-Attention
Multi-Head Attention
Positional Encoding
Encoder-Decoder
Transformer Workflow
Lab: Attention visualization
Lab: Transformer demo
03
LLMs and Foundation Models
▼
GPT-4o
Claude
Gemini
Llama
Mistral
Model Selection
Context Windows
API Comparison
Lab: Compare LLM outputs
Lab: Business use cases
04
Advanced Prompt Engineering
▼
Zero-shot
Few-shot
Chain of Thought
ReAct
Tree of Thought
Prompt Templates
Role Prompting
Lab: Prompt optimization
Lab: Reasoning prompts
05
Embeddings & Vector Databases
▼
Tokenization
Embeddings
Vector Similarity
FAISS
Semantic Search
Chunking
Lab: Build semantic search
06
Retrieval-Augmented Generation (RAG)
▼
Document Loading
Chunking
Embeddings
Retrieval
Generation
RAG Pipeline
Evaluation
Lab: Build PDF chatbot
07
LangChain Fundamentals
▼
Chains
Prompts
Memory
Tools
Agents
Document Loaders
Output Parsers
Lab: Build LangChain apps
08
OpenAI API & AI Applications
▼
Chat Completions
Embeddings API
Image Generation
Function Calling
Streaming
Best Practices
Lab: Build AI assistant
09
Enterprise Mini Project
▼
Enterprise AI Assistant
Documentation
Deployment Basics
Presentation
Lab: Develop complete AI application
10
Assessment & Certification
▼
Module Quizzes
Coding Assignments
Prompt Engineering Assessment
RAG Implementation
LangChain Assessment
Enterprise Project
Viva
Final Certification Test
📄 GENAI-ENG — GenAI Engineer Track (210 Hours)
L3
GENAI-ENG
GenAI Engineer Track (210 Hours)
01
Python — Basic to Advanced Concepts
▼
Python Fundamentals: Variables, Data Types, Control Flow, Functions, OOP, File I/O
Good Code Practices: PEP 8, type hints, docstrings, Ruff, Black, pre-commit, venv/poetry
Python for Data & AI: NumPy, Pandas, Matplotlib, async/await, REST with httpx
Lab: Hands-on Python for AI workflows
02
Transformer Architecture
▼
Self-Attention math
Multi-Head Attention
Positional Encoding
Encoder-Decoder
Layer Norm & Feed-Forward sublayers
Lab: Transformer internals exercises
03
Introduction to GenAI and LLM Models
▼
GenAI Landscape & LLM Families
GPT-4o, Claude, Gemini, Mistral, Llama
Capabilities, context windows, pricing, use-case fit
OpenAI & Anthropic API quickstart
Lab: Model comparison and API setup
04
Encoding, Tokenization & Embeddings
▼
BPE & WordPiece tokenizers
tiktoken
text-embedding models
Cosine similarity & embedding drift
FAISS flat vs HNSW index
Lab: Embedding and similarity search
05
Fine-Tuning and Prompt Engineering
▼
Zero-shot, Few-shot, Chain-of-Thought, ReAct, Tree-of-Thought
System vs User roles & Jinja2 prompt templating
SFT, LoRA, QLoRA, PEFT
HuggingFace Trainer & RLHF overview
Lab: Prompt mastery and fine-tuning drills
06
RAG Systems
▼
Naive RAG pipeline: loaders, chunking, FAISS, generation
Advanced RAG: HyDE, Parent-Child, Multi-Query, Re-ranking
RAGAS evaluation & LangSmith tracing
Hallucination detection
Lab: Production-style RAG build
07
Text-to-SQL Systems
▼
NL → SQL fundamentals & schema grounding
Few-shot schema prompting
SQL validation, correction & safety guardrails
LangChain SQL agents & LangGraph SQL workflows
Lab: Build a BI / Text-to-SQL assistant
08
LangChain and LangGraph
▼
LangChain components and tools
StateGraph, Nodes, Edges, Conditional routing
Checkpointing with MemorySaver
Human-in-the-loop & time-travel debugging
Lab: LangGraph workflow build
09
Agents, Multi-Agent and Deep Agents
▼
ReAct agents & custom tool definitions
Supervisor and Swarm multi-agent patterns
Agentic memory & long-horizon planning
CrewAI orchestration patterns
Lab: Multi-agent collaboration project
10
Evaluation Strategies
▼
RAGAS, DeepEval, PromptFoo, LangSmith Evals
Metric taxonomy & regression testing
CI/CD eval gates & LLM-as-judge
Lab: Evaluation harness setup
11
Guardrails
▼
Guardrails AI & NeMo Guardrails
Pydantic validators & output parsers
Hallucination detection & PII redaction
Off-topic detection
Lab: Guardrail policy implementation
12
MCP (Model Context Protocol)
▼
MCP architecture & server design
Resources, Tools, Prompts primitives
MCP clients & multi-server hub patterns
A2A protocol & combined architecture
Lab: MCP server and agent integration
13
Coding Assistants — Cursor, Claude, Copilot
▼
Cursor IDE features
Claude Code CLI & GitHub Copilot
Prompt-driven refactoring & test generation
Code review automation
Lab: AI-powered development workflow
14
AIOps / LLMOps
▼
MLOps & LLMOps foundations
MLflow, DVC, Docker, CI/CD for ML
Production monitoring & cost optimisation
Semantic cache & token-budget controls
Lab: Monitoring and cost dashboard
15
Frontend and Backend — Streamlit & FastAPI
▼
FastAPI async endpoints & JWT auth
WebSocket streaming for LLMs
Streamlit & Chainlit chat UIs
File upload & real-time streaming display
Lab: Full-stack AI app UI
16
End-to-End Project with Deployment
▼
Microservices architecture for LLM apps
Docker + Docker Compose deployment
Secrets management & cloud deploy
Responsible AI & bias auditing
Lab: Capstone deployment and defense