Generative AI for Everyone
Discover how generative AI creates text, images, and ideas—empowering anyone to boost creativity, productivity, and problem‑solving with modern AI tools.
Duration: 1 Day
Hours: 3 Hours
Training Level: All Levels
Batch One
Wednesday, March 04, 2026
11:00 AM - 02:00 PM (Eastern Time)
Batch Two
Wednesday, April 08, 2026
11:00 AM - 02:00 PM (Eastern Time)
Batch Three
Wednesday, May 13, 2026
11:00 AM - 02:00 PM (Eastern Time)
About the Course:
This course is a beginner-friendly introduction to Generative Artificial Intelligence, designed for anyone who wants to understand what GenAI is, how it works, and why it matters, without technical depth or coding.
Participants will explore Generative AI as a general-purpose technology, understand large language models as thought partners, and see how GenAI is transforming software, work, and society. The course balances conceptual understanding, real-world examples, and responsible AI thinking, making it suitable for both professionals and general audiences.
Course Objectives:
By the end of this 3-hour course, participants will be able to:
- Understand what Generative AI is and how it works.
- View LLMs as collaborative thinking tools.
- Recognize Generative AI as a general-purpose technology.
- Identify common Generative AI applications.
- Understand how GenAI is used in software products.
- Gain intuition about GenAI project lifecycles and costs.
- Understand key concepts like RAG, fine-tuning, and model selection.
- See how GenAI is reshaping work, jobs, and workflows.
- Think critically about risks, responsibility, and the future of AI.
Who is the Target Audience?
This course is ideal for:
- Working professionals from any domain
- Business leaders and managers
- Students and career switchers
- Educators and trainers
- Entrepreneurs and founders
- Anyone curious about AI and its impact
- No technical or AI background required.
Basic Knowledge:
Participants should have:
- Basic digital literacy.
- Familiarity with using web applications and software tools.
- Curiosity about technology and its impact.
- No coding, math, or AI knowledge is expected.
Curriculum
Total Duration: 3 Hours
What is Generative AI
- Definition and simple examples
- How Generative AI differs from traditional AI
- Why Generative AI feels “human-like.”
How Generative AI Works
Data, models, and learning (high-level view)
Training vs inference (conceptual understanding)
Why Generative AI can generate text, images, and more
LLM as a Thought Partner
- From tools to collaborators
- Brainstorming, reasoning, and idea generation
- Strengths and limitations of LLMs
AI as a General Purpose Technology
- Comparison with electricity and the internet
- Why GenAI impacts every industry
- Long-term economic and social implications
Generative AI Applications
- Content creation
- Research and analysis
- Education and learning
- Customer support and assistance
Generative AI in Software Application
- How GenAI is embedded into modern software
- Copilots, assistants, and smart features
- Examples from everyday tools
Lifecycle of Generative AI Project
- Problem identification
- Data and model selection
- Deployment and iteration
- Monitoring and improvement
Cost Intuition
- What drives GenAI costs
- Compute, data, and usage
- Why “token usage” matters (conceptual)
Retrieval Augmented Generation (RAG)
- Why LLMs need external knowledge
- How RAG improves accuracy and relevance
- Business use cases of RAG
Fine Tuning
- What fine-tuning means (simple explanation)
- When fine-tuning is useful
- Fine-tuning vs prompting
Pretraining the LLM
- What pretraining means
- Why pretraining is expensive
- Why most organizations don’t pretrain models
Choosing a Model
- Open vs closed models
- Size vs performance trade-offs
- Cost, capability, and safety considerations
Generative AI and Business
- How businesses are adopting GenAI
- Competitive advantage and productivity
- Risks of not adopting GenAI
Day-to-Day Usage of Web UI LLMs
- Using tools like ChatGPT, Copilot, and Gemini
- Practical daily tasks
- Best practices for everyday use
Task Analysis of Jobs
- Breaking jobs into tasks
- Identifying AI-assisted tasks
- Human judgment vs AI execution
New Workflows and New Automation Potential Across Sectors
- How workflows are changing
- Industry examples
- Human-in-the-loop systems
Concern About AI
- Job displacement fears
- Reliability and hallucinations
- Overdependence on AI
Artificial General Intelligence
- What AGI means
- Current reality vs hype
- Why AGI is still a long-term concept
Responsible AI
- Fairness, transparency, and accountability
- Ethical use of Generative AI
- Individual and organizational responsibility
Building a More Intelligent World
- Human + AI collaboration
- Augmenting intelligence, not replacing it
- Future outlook and closing thoughts