Generative AI Course

What is Generative AI?

Generative AI, or generative artificial intelligence, creates new content from patterns it learned during training. Ask it for a summary and it writes one that did not exist before. Traditional AI classifies or predicts from existing data. Generative AI produces something new. ChatGPT, Gemini, Claude and Midjourney are all generative AI applications. People also call it genAI or gen AI. The terms all mean the same thing.

Why Learn Generative AI?

Indian companies have moved past pilots. A survey of more than 2,500 businesses across five countries found 62% of Indian firms already using generative AI tools. That is the highest adoption rate among the five.

Talent supply has not kept up pace with the growth. The NASSCOM and Deloitte India Study on AI skills projects the talent pool to cross 1.25 million by 2027. This talent count sat near 600,000 in 2022, which works out to roughly 15% growth a year. While the Indian AI market is expected to grow at 25% to 35% a year over the same period, talent growth is at 15% only.

You can see that shortfall in current job descriptions. Roles now ask for prompt design, workflow automation and output evaluation alongside domain skills. Employers are hiring for system design, not tool use.

Prompting well saves you time on one task. Automating the workflow removes the task from your week. That second skill is what companies are short of, and learning it now puts you ahead of the supply.

What You Will Learn?

After completing this Generative AI course, you will be able to do six key things. You practice each one on live tasks.

• How large language models actually behave
  • A large language model, or LLM, predicts the next piece of text from patterns in its training data.
  • You learn what that design makes it good at, and where it reliably falls over.
  • You compare GPT, Claude, Gemini and LLaMA on the same task and see the differences yourself.
• Prompt engineering that survives real use
  • You practice zero-shot, few-shot, role-based and chain-of-thought prompting on live tasks.
  • You build prompt structures you can reuse instead of rewriting from scratch each time.
  • You learn to hold context across a multi-turn conversation without the model drifting.
• Output evaluation and quality control
  • Generated text reads confidently even when it is wrong. That is the expensive part.
  • You set up checks that catch hallucination before the output reaches a stakeholder.
  • You learn where a human must stay in the loop and where automation is safe.
• No-code workflow automation
  • You connect AI to the tools your work already runs on, without writing code.
  • You build automations in Zapier, n8n and Make.com and test them against real inputs.
  • You wire Notion, Google Docs and Slack into a single working flow.
• AI assistants without writing code
  • An AI agent plans its own steps, calls other tools, and remembers context. A chatbot does none of that.
  • You learn the four components that make an agent work: memory, planning, tool use and autonomy.
  • You orchestrate agents with no-code tools and review how the leading agent frameworks approach the same problem.
• Responsible AI you can defend in a review
  • You learn where bias, hallucination and data leakage enter a workflow.
  • You apply guardrails and human-in-the-loop checks to your own capstone build.
  • You reference the EU AI Act and NITI Aayog’s responsible AI approach when a compliance question lands.