Module 1: Introduction to Generative AI
What is Generative AI?
Evolution of AI and foundation models
Generative AI vs Traditional AI
Key applications and industry use cases

Module 2: Foundations of Large Language Models
How LLMs work (tokens, embeddings, transformers)
Training vs inference
Model limitations and ethics
Bias, safety, and responsible AI

Module 3: Generative AI Models & Tools
Text models (ChatGPT, Claude, Gemini)
Image generation models (DALL·E, Midjourney, Stab
le Diffusion)
Code generation tools
Multimodal AI overview

Module 4: Prompt Engineering Basics
Prompt structure and best practices
Zero-shot and few-shot prompting
Role-based and contextual prompts
Output formatting and control

Module 5: Advanced Generative AI Techniques
Chain-of-thought reasoning
Multi-step and agent-based workflows
Retrieval-Augmented Generation (RAG)
Fine-tuning vs prompting

Module 6: Generative AI for Business & Technology
GenAI in software development
GenAI for data analysis and reporting
Marketing, content, and design automation
GenAI in operations and decision support


Module 7: Integration & Automation
Using GenAI with APIs
Workflow automation with AI
AI assistants and chatbots
Cloud and enterprise integrations

Module 8: Evaluation, Security & Governance
Measuring output quality
Cost and performance optimization
Data privacy and security
AI governance and compliance

Module 9: Hands-On Labs & Case Studies
Real-world use cases

Prompt-driven automation labs
Industry case studies

Module 10: Capstone Project
Build a Generative AI solution for a real problem
End-to-end design and deployment