How GenAI models work (Understand with real life example)

In today’s fast-paced digital world, you're probably hearing a lot about Generative AI, or Gen AI. From AI writing essays to creating stunning artwork and even composing music — Gen AI is making waves. But how does it all work? Why is it such a big deal? Let’s break it down together.

Overview of GenAI models

Gen AI models are a type of artificial intelligence that can generate new content — like text, images, audio, and even code — by learning from massive amounts of existing data. Unlike traditional AI, which often just analyzes or classifies data, Gen AI creates something entirely new. Think of it like an artist trained on millions of paintings who then paints something original based on what they’ve learned.

Whether you’re a student, a developer, a marketer, or just curious, understanding how Gen AI works can give you an edge. It’s transforming industries, changing the way we interact with technology, and it’s only getting smarter. The more you know about it, the better prepared you'll be for the future.

Basics of Gen AI Models

Gen AI models are smart algorithms designed to generate new content by understanding patterns in data. There are different types, like:

• Language models (e.g., GPT – generates text)

• Image models (e.g., DALL·E – creates images from text)

• Audio models (e.g., Jukebox – generates music)

Key components and algorithms

At the heart of Gen AI are techniques like

• Neural networks— Inspired by the human brain, these help the model “think.” We can talk about this in detail.

• Transformers— a powerful architecture that enables the model to focus on important data and generate high-quality outputs.

• Attention mechanisms— they help the model decide which parts of the input matter most when generating content.

Gen AI and Traditional AI Differences

How Gen AI Models Function

1. Data input and preprocessing

First, Gen AI needs to be “fed” data — whether it’s text, images, or audio. This data is cleaned and formatted so the model can understand it.

2. Learning and Training

The model goes through training, where it learns from patterns in the data. It adjusts/updates internal settings (like weights) to minimize errors.

3. Generation and Output

Once trained, the model can take a prompt (like "Write a poem about spring") and generate a new, unique response based on what it learned.

Real-Life Example

Let’s look at GPT (like ChatGPT)—a popular Gen AI model.

Data preparation

To build a model like GPT (Generative Pre-trained Transformer), the first step is to prepare an enormous amount of training data.

Each model has its own vocab size, which helps them with tokenization. ChatGPT updates the vocab size with their new models GPT-4 has about 100,256 tokens, while GPT-4o has about 199,997 tokens.

• Data Size: GPT-3 was trained on 570GB of text data, while GPT-4 likely exceeds that (exact size not publicly confirmed).

• Sources: books, Wikipedia, common Crawl (publicly available websites), OpenWebText, code, scientific papers, etc.

Preprocessing Tasks:

• Tokenization: Breaking text into smaller pieces (tokens)—words, characters, or subwords.

• Cleaning: Removing duplicates, spam, harmful content, or anything irrelevant.

• Formatting: Ensuring uniform encoding, sentence structure, and metadata tagging.

Example: “Write a birthday message” → ["Write", "a", "birthday", "message"]

Model training

GPT is trained using a technique called unsupervised learning combined with self-supervised prediction.

• Transformer (introduced by Vaswani et al., 2017): The backbone architecture.

• Attention Mechanism: Helps the model "focus" on important words when predicting the next word.

• Parameters: GPT-3 has 175 billion parameters, GPT-4 has even more (not disclosed).

Objective:

Train the model to predict the next word/token in a sequence.

• Example: Input → “The cat sat on the” → Model predicts next word “mat”

Training Process:

• Uses massive GPU/TPU clusters for computation (e.g., NVIDIA A100, TPUv4).

• Optimization with the Adam optimizer and cross-entropy loss.

• Cost: Training GPT-3 cost over $4.6 million USD in compute.

Example

Let’s say a user types:

“Write a birthday message for my 10-year-old sister who loves space.”

What Happens Internally:

1. Input is tokenized and passed into the model.

2. The model computes context and relationships between tokens using attention layers.

3. It generates output tokens one by one — predicting each next word based on the previous.

The model produces a result like

"Happy 10th Birthday! 🎉 Wishing you a day as bright and amazing as the stars you love. May your dreams reach the moon and beyond!"

Key Techniques in Generation

• Sampling Methods:

  • Greedy Search (always pick most probable token)

  • Top-k Sampling (choose from top-k most likely tokens)

  • Top-p (nucleus) Sampling (choose from top cumulative probability tokens)

• Temperature Parameter: Controls randomness in output.

  • High temp → More creative

  • Low temp → More factual

Applications of Gen AI Models in Various Fields

Gen AI is everywhere! Some major areas include:

• 🎥 Content Creation: Writing articles, generating videos, composing music.

• 🩺 Healthcare: Summarizing medical notes, predicting patient needs.

• 💰 Finance: Automating reports, risk assessment, fraud detection.

• 💬 Customer Service: AI chatbots, virtual assistants, and email drafting.

Challenges and Limitations

Like any powerful tool, Gen AI also has its downsides

Ethical concerns

Deepfakes, misinformation, plagiarism.

Computational power requirements

Training large models requires tons of computing power.

Data privacy and security issues

Models can memorize and leak sensitive data.

Model bias and fairness

If the training data is biased, the output might be too.

Conclusion

At the heart of it, Generative AI like GPT isn’t just about fancy tech — it’s about teaching machines to understand us and create with us.

We’ve walked through how a simple prompt like “write a birthday message” turns into a thoughtful, creative response. Behind the scenes, there’s a lot going on: huge amounts of data, complex training, and smart algorithms all working together to generate something meaningful.

But you don’t need to be a data scientist to appreciate the magic. Whether you’re using Gen AI to write, design, solve problems, or just have fun — understanding how it works helps you get the most out of it.

As this technology keeps evolving, one thing’s clear: Gen AI isn’t here to replace human creativity — it’s here to amplify it. And the more we learn about it, the more powerful (and responsible) we become as creators, innovators, and users.

Gen AI is not here to replace humans — it’s here to empower us. The future will belong to those who understand and harness its potential.

Thanks for reading; drop a comment if it is informative for you.