Skill Development
Generative AI
Master Generative AI tools and build LLM-powered applications. Learn prompt engineering, RAG pipelines and AI automation.
4.8 rating
400+ enrolled
2 Months
Beginner to Advanced
Prerequisite Knowledge: NONE | Education Background: ANY
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This program includes
Industry certificate
Live + recorded sessions
Job placement support
Lifetime access to material
Small batch — personal attention
15+ language support
Recognition
Certification
Official Certification
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Generative AI
Industry recognised certificate
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What You’ll Achieve
Course Outcomes
Build text, image and code generation applications using LLMs
Master prompt engineering for ChatGPT, Claude and Gemini
Fine-tune open-source models like LLaMA and Mistral
Build RAG (Retrieval Augmented Generation) pipelines
Create AI-powered chatbots and automation workflows
Deploy GenAI applications using APIs and cloud platforms
Understand Diffusion models and image generation (DALL·E, Stable Diffusion)
Get job-ready for GenAI Engineer and Prompt Engineer roles
Skill Coverage
Skill-Sets Covered in Generative AI Program
Large Language Models
Prompt Engineering
Image Generation
AI Chatbot Development
RAG Pipelines
Model Fine-Tuning
API Integration
AI Workflow Automation
Tools & Technologies You Will Learn
Python
PyTorch
Jupyter
FastAPI
Docker
Google Cloud
AWS
PandasOpenAI API, LangChain, HuggingFace & more
Prerequisite Knowledge
BASIC PYTHON
Education Background
ANY
Age Group
18+ Years
Mode
Classroom + Online
Curriculum
Course Syllabus
1Introduction to Generative AI
- What is Generative AI and how it differs from traditional AI
- Overview of GPT, DALL·E, Stable Diffusion, Claude, Gemini
- Real-world GenAI applications across industries
- Setting up your GenAI development environment
2Prompt Engineering
- Zero-shot, few-shot and chain-of-thought prompting
- Prompt templates and structured outputs
- System prompts and role-based prompting
- Advanced prompting for coding, writing and analysis
3Large Language Models (LLMs)
- Transformer architecture and attention mechanism
- GPT-4, LLaMA, Mistral, Gemini — comparison and use cases
- Tokenization, embeddings and vector databases
- OpenAI API and HuggingFace integration
4RAG — Retrieval Augmented Generation
- What is RAG and why it matters
- Vector databases — Pinecone, ChromaDB, FAISS
- Building a document Q&A chatbot with RAG
- LangChain framework for RAG pipelines
5Fine-Tuning & Model Customisation
- When to fine-tune vs prompt engineer
- LoRA and QLoRA fine-tuning techniques
- Fine-tuning LLaMA on custom datasets
- Evaluating fine-tuned model performance
6Image Generation & Multimodal AI
- Diffusion models — DALL·E, Stable Diffusion, Midjourney
- Text-to-image and image-to-image techniques
- Vision-language models (GPT-4V, LLaVA)
- Real-world project: AI design automation tool
7Building GenAI Applications
- Building AI chatbots with memory and context
- AI content generation tools for blog, social and email
- Code generation and AI pair programming
- Deploying GenAI apps with FastAPI and Streamlit
8Ethics, Safety & Capstone Project
- AI hallucinations, bias and safety guardrails
- Responsible GenAI development practices
- End-to-end capstone — build and deploy a GenAI product
- Portfolio building and interview preparation
