● To understand the basics of Generative AI and how machines learn to generate new content.
● To explore popular AI tools for text, image, voice, and video generation.
● To develop practical skills through hands-on mini-projects and creativity tasks.
● To prepare students for future opportunities in AI careers, freelancing, and innovation.
What you'll learn
Day & Date Duration (time) Session Topic Resource Person
Day 1 2 hrs Session 1 Introduction to Generative AI
Day 2 2 hrs Session 2 How Generative AI Works
Day 3 2 hrs Session 3 Prompt Engineering
Day 4 2 hrs Session 4 Text Generation Tools
Day 5 2 hrs Session 5 Image Generation Tools
Day 6 2 hrs Session 6 AI for Audio & Voice
Day 7 2 hrs Session 7 Video Generation with AI
Day 8 2 hrs Session 8 AI for Coding & Development
Day 9 2 hrs Session 9 Build Simple AI Applications
Day 10 2 hrs Session 10 Ethical AI & Future of Jobs
Day 11 2 hrs Session 11 Mini Project Work
Day 12 2 hrs Session 12 Project Presentation & Certification
After completing this course, students will be able to:
1. Explain what Generative AI is, how it works, and its real-life applications.
2. Create AI-generated content (text, images, audio, video, and presentations).
3. Build simple generative AI projects & prototypes for problem-solving.
4. Present and pitch AI-based innovative ideas with confidence.
1. What is Generative AI and how is it different from traditional AI?
Generative AI focuses on creating new content such as text, images, code, audio, or videos using learned patterns from data, whereas traditional AI mainly focuses on prediction, classification, or decision-making tasks.
2. What skills can students gain from a Generative AI Lab?
Students learn prompt engineering, model fine-tuning basics, AI-assisted coding, content generation, ethical AI practices, and how to integrate Generative AI models into real-world applications.
3. Which tools and technologies are commonly used in a Generative AI Lab?
Popular tools include Python, TensorFlow or PyTorch, Hugging Face models, OpenAI APIs, LangChain, Power BI or Streamlit for visualization, and cloud platforms like Google Colab or Azure.
4. How does Generative AI help in creating smarter systems?
Generative AI enables automation, intelligent content creation, decision support, personalization, and rapid prototyping, leading to systems that adapt, learn, and innovate efficiently.
5. What are the ethical considerations when using Generative AI?
Key concerns include data privacy, bias in generated content, intellectual property issues, misuse of AI-generated outputs, and the need for transparency and responsible deployment.
