Generative AI Tutorial

First — What exactly is Generative AI?

Generative AI is a type of artificial intelligence that can create completely new things — things that did not exist before — such as:

• A paragraph of text
• A poem or story
• A picture or artwork
• A piece of music
• Computer code
• A short video clip
• Even 3D models or synthetic voices

The key word is create new content that looks/sounds/feels realistic, like something a human could have made.

Very important comparison — Generative AI vs Discriminative (Traditional) AI

This is where most people get confused, so let’s clear it right now with a super simple analogy.

Imagine you show 1,000 photos of cats and dogs to two different students:

Student A (Discriminative AI) Learns to draw an invisible line between cats and dogs. After training, when you show a new photo, Student A says: → “This is a cat” or “This is a dog” (classification) → Or “80% chance it’s a cat”

Examples of discriminative AI (very common before 2022):

• Spam filter in Gmail
• Face recognition on your phone
• Medical AI that says “tumor / no tumor”
• Netflix “Will you like this movie? Yes/No probability”

Student B (Generative AI) Instead of drawing a line, this student learns how cats really look — fur patterns, eye shapes, ear positions, tail styles, backgrounds they usually appear in. After training, you say: “Draw me a cute orange cat wearing sunglasses on a beach at sunset.” And Student B creates a brand-new picture that never existed before!

So:

How does Generative AI actually learn to create?

Modern generative AI (2022–2026 era) mostly uses two big ideas:

1. Predict the next word/pixel/sound (very simplified view)
   • Trained on trillions of words / millions of images
   • Task during training: “Given these words, what is the most likely next word?”
   • After doing this billions of times → it learns grammar, facts, style, logic, creativity
2. Start from noise → slowly make it clear (this is how most image generators work — called diffusion models)

Think of it like this:

You give the AI pure TV static (random noise). It slowly removes the noise step-by-step (50–100 steps), guided by what it learned about real images. At the end — beautiful realistic picture appears!

That’s why tools like Midjourney, Stable Diffusion, Flux, DALL·E work.

Popular Types of Generative AI (2025–2026 view)

Real-life Everyday Examples You Can Try Today

Example 1 – Text (most people start here)

Prompt to any chatbot (ChatGPT, Claude, Gemini, Grok, etc.):

“Write a funny 4-line poem about a software engineer who falls in love with coffee.”

Typical output you might get:

My code was clean, my bugs were few, Till coffee whispered, “I love you.” One espresso later, heart in a spin, Now commits say “push –love begin” ☕😂

Example 2 – Image Generation

Go to any free image generator (e.g. flux1.ai, ideogram.ai, Leonardo.ai free tier):

Prompt: “A cyberpunk street food stall at night in future Tokyo, neon signs in Japanese and English, steam rising from ramen bowl, reflective wet street, cinematic lighting, highly detailed, 8k”

→ You get a completely new image no human has ever drawn before.

Example 3 – Code

Prompt: “Write a Python function that takes a list of numbers and returns only the prime numbers — also add type hints and docstring”

It will write clean, working code.

Quick Summary Table – When to use Generative AI

So — Generative AI = AI that dreams up new things instead of just labeling or deciding.

Got it? 😄

Now tell me — which part would you like to go deeper into?

• How to write better prompts?
• Image generation step-by-step?
• The math behind transformers & diffusion?
• Risks & limitations?
• Best free tools in 2026?

Your turn! 🚀