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Domain 03 . Data & AI

Advanced Generative AI

—
Hours of teaching
Advanced
Level
18
Modules
Classroom . Online . Hybrid
Mode
Course overview

This programme takes learners from the foundations of artificial intelligence to production-grade generative AI systems. It covers the mathematics and Python engineering that underpin modern models, the architecture of large language models, and the applied disciplines that surround them: prompt engineering, retrieval-augmented generation, agent design, fine-tuning, multimodal generation, evaluation, security and MLOps.

Every module pairs concepts with a lab on the tools used in industry — Hugging Face, LangChain, LlamaIndex, vector databases, FastAPI and Docker — and the programme closes with an end-to-end capstone build.

Who it is for
  • Software developers moving into AI engineering
  • Data science and analytics professionals
  • Final-year engineering and MCA students
  • Technical leads evaluating AI for their products
Prerequisites
Basic programming knowledge. Python fundamentals recommended; the programme revises them in Module 3.
How it runs
Learn → Practise → Build → Experience → Demonstrate, ending in a capstone. Delivered by practitioners from the engineering bench, in Madurai, Coimbatore and online.
Final project
AI Web Application

Programme sheet

The printed sheet carries the full module breakdown, labs, project work and certification path. Fees, dates and formats for the next intake are confirmed by the education team on enquiry.

Download the programme sheet →Enquire or apply →

Learning outcomes

01
Explain how transformer-based language models are built, scaled and served.
02
Write, chain and optimise prompts for reliable production behaviour.
03
Build retrieval pipelines over private documents using vector databases.
04
Design autonomous and multi-agent systems with tool and function calling.
05
Fine-tune open models with LoRA, QLoRA and PEFT on prepared datasets.
06
Generate and integrate image, audio and speech output alongside text.
07
Deploy AI applications as APIs and containers and monitor them in use.
08
Evaluate output with BLEU, ROUGE, perplexity and LLM-as-a-judge methods.
09
Identify and mitigate prompt injection, bias, privacy and governance risks.

Module structure

18 modules
Module 01

Foundations of Artificial Intelligence

Evolution of AI . Machine Learning vs Deep Learning vs Generative AI . AI Applications across Industries . AI Ecosystem . AI Ethics and Responsible AI
Lab
Setting Up AI Development Environment . Introduction to Google Colab, Jupyter Notebook . Using Python for AI
Module 02

Mathematics for Generative AI

Linear Algebra . Probability & Statistics . Optimization . Gradient Descent . Loss Functions
Lab
NumPy . Matrix Operations . Data Visualization Using Matplotlib
Module 03

Python for AI Development

Python Fundamentals . Data Structures . Functions . OOP . File Handling . Exception Handling . Virtual Environments
Lab
pandas . NumPy . Matplotlib . Seaborn . scikit-learn Basics
Module 04

Natural Language Processing (NLP)

Text Processing . Tokenization . Stemming . Lemmatization . Word Embeddings . TF-IDF . Word2Vec . FastText . BERT Introduction
Lab
NLTK . spaCy . Hugging Face Tokenizers
Module 05

Deep Learning Fundamentals

Artificial Neural Networks . RNN . LSTM . GRU . Transformers . Attention Mechanism
Lab
TensorFlow . Keras . PyTorch Basics
Module 06

Large Language Models (LLMs)

GPT Architecture . Transformer Architecture . Encoder vs Decoder . Positional Encoding . Attention Layers . Token Prediction . Model Scaling
Popular Models — GPT, Claude, Gemini, Llama, DeepSeek, Mistral, Qwen
Lab
Hugging Face Transformers . Model Inference
Module 07

Prompt Engineering (advanced)

Zero-shot Prompting . One-shot Prompting . Few-shot Prompting . Tree-of-Thought . Role Prompting . System Prompt Design . Prompt Chaining . Prompt Optimization
Mini Projects — AI Tutor, AI HR Assistant, AI Customer Support Bot
Lab
ChatGPT . Claude . Gemini . Perplexity
Module 08

Retrieval-Augmented Generation (RAG)

Why RAG? . Document Loading . Embeddings
Vector Databases — Semantic Search, Retrieval Pipelines, Context Injection, FAISS, ChromaDB, Pinecone, Weaviate
Project — PDF Chatbot, Company Knowledge Assistant
Lab
LangChain . LlamaIndex . ChromaDB
Module 09

AI Agents

Agent Architecture . Autonomous Agents . Planning . Memory . Tool Calling . Function Calling . Multi-Agent Systems
Frameworks — LangGraph, CrewAI, AutoGen, OpenAI Agents SDK
Lab
Build AI Research Agent . Travel Planner Agent . Email Automation Agent
Module 10

Fine-Tuning LLMs

Instruction Tuning . LoRA . QLoRA . PEFT . RLHF . DPO
Lab
Fine-Tuning with Hugging Face . Dataset Preparation
Module 11

Multimodal AI

Text Generation . Image Generation . Video Generation . Audio Generation . Speech Models . Vision Models
Models — DALL-E, Stable Diffusion, Flux, Midjourney, Whisper, ElevenLabs
Lab
Image Generation . Speech-To-Text . Text-To-Speech
Module 12

AI Application Development

REST APIs . FastAPI . Flask . Streamlit . Gradio . Docker . Deployment Basics
Module 13

AI Automation

Workflow Automation . AI Assistants . Email Automation . Document Automation . OCR Integration . os
Tools — Zapier, n8n, Make, Power Automate
Module 14

AI Security & Responsible AI

AI Risks . Prompt Injection . Jailbreaking . Hallucinations . Model Bias . Data Privacy . AI Governance . Secure AI Deployment
Module 15

Model Evaluation

BLEU . ROUGE . Perplexity . Human Evaluation . LLM-as-a-Judge . Benchmarking
Module 16

MLOps for Generative AI

Model Versioning . Experiment Tracking . Monitoring . Scaling . GPU Deployment
Tools — MLflow, Weights & Biases, Docker, Kubernetes, GitHub Actions
Module 17

Cloud AI Services

OpenAI API . Azure OpenAI . Google Vertex AI . AWS Bedrock . Hugging Face Inference API
Lab
API Integration . Secure Key Management . Cost Monitoring
Module 18

Capstone Projects

Students build end-to-end Generative AI solutions, such as: . Enterprise RAG Chatbot . AI Legal Assistant . AI Medical Assistant . AI Resume Analyzer . AI Interview Coach . AI Code Generator . AI Content Creator . AI Customer Support Bot . AI Document Summarizer . AI Research Assistant . AI Voice Assistant . AI Sales Assistant

Assessment & certification

Module assignments and labs
25%
Mini projects
20%
Internal assessments
15%
Capstone project and review
40%

Learners who complete all modules, submit the capstone project and clear the final review receive a course completion certificate from Kaizen Infinities Private Limited. Project work is documented for the learner's portfolio and GitHub profile, and interview preparation is included in the final week.

Career outcomes . Roles this programme prepares for
Generative AI EngineerLLM Application DeveloperAI Agent DeveloperPrompt EngineerNLP EngineerAI Product EngineerMLOps EngineerAI Automation Consultant
Tools & technologies covered
PythonVS CodeGoogle ColabJupyter NotebookGitHubHugging FaceLangChainLlamaIndexChromaDBFAISSStreamlitGradioFastAPIDocker
Enquire or apply →Programme sheet (PDF)Institutions can commission a cohort

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