Generative AI Explained: From ChatGPT to Midjourney and How They Actually Work
By Evangelos Bolofis, AI Expert at ebolofis.ai
In the last few years, “Generative AI” has exploded from a niche academic field into a global phenomenon. We now have technology that can write poetry, create photorealistic images from a sentence, and compose music.
But what is it? How does a machine “create” something new and seemingly original?
This is the ebolofis.ai deep dive into the elegant science behind Generative AI.
The Core Concept: Learn the Pattern, Then Generate More of It
At its heart, a generative model is a sophisticated pattern-recognition machine. It is trained on a massive dataset of human-created content (e.g., all the text on the public internet, or millions of digital images) and it learns the underlying structure, style, and implicit rules of that data.
Once trained, it can then generate new content that conforms to those learned patterns.
Let’s look at the two most famous examples:
1. How ChatGPT (and other Large Language Models) Work
Large Language Models (LLMs) are trained on trillions of words. They don’t “understand” text like a human; they learn the statistical probability of which word, or piece of a word, should come next.
- The Analogy: Autocomplete on Steroids. Think of the autocomplete on your phone. When you type “I’m heading to the,” it might suggest “gym,” “store,” or “office” because it has learned which words commonly follow that phrase. An LLM does the same thing, but on an unbelievably massive and complex scale. When you ask it, “What is the capital of France?”, it is essentially predicting the most statistically likely sequence of words to follow that question, which is: “The capital of France is Paris.”
- The Magic: The “magic” emerges because by learning these statistical relationships across trillions of examples, the model inadvertently captures the patterns of grammar, facts, and even basic reasoning.
2. How Midjourney (and other Image Diffusion Models) Work
Image generators like Midjourney, DALL-E, and Stable Diffusion often use a brilliant process called a diffusion model.
- The Analogy: Perfectly Restoring a Noisy Photo. Imagine you take a perfect, clear digital photo of a cat. Now, you use a program to slowly add a little bit of random noise (pixelated static) to it, step by step, until the original image is completely gone—it’s just pure static. A diffusion model is trained to do the exact reverse. It learns how to take a noisy, static-filled image and, step-by-step, remove the noise to perfectly restore the original clear image.
- The Magic: To generate a new image from a text prompt like “a photo of a cat wearing a tophat,” the model starts with pure random noise and, guided by the meaning of your text, it “denoises” that static into a brand new image that matches your description. It isn’t finding a photo online; it’s constructing one from its learned concepts of “cat,” “photo,” and “tophat.”
Why is Generative AI Such a Big Deal?
For decades, AI was primarily analytical—it could classify data, find anomalies, and make predictions. It was about understanding the world as it already existed.
Generative AI is different. It’s creative. It can synthesize new ideas, new designs, and new solutions. This shifts AI from being a tool just for analysts to being a co-pilot for creators, marketers, designers, and engineers. It’s a fundamental leap, and we are only just beginning to explore its potential.
At ebolofis.ai, we’ll be tracking every step of this new creative revolution. Stick with us.
Evangelos Bolofis | The AI Intersection 