GenAI models can create new content from nothing — text, images, music, even videos. To do so, it uses lots of fascinating technologies.
Let’s learn:
Generative AI is a type of computer program that learns patterns from the data it's trained on. This program can then use those patterns to generate new content.
GenAI is a subset of AI technologies. The other prominent group being traditional AI.
👉 Traditional AI systems are used to recognize patterns in existing data.
👉 GenAI systems are used to create something new from nothing.
In other words:
The model, created through the process we outlined above, is a massive mathematical function with (often) trillions of adjustable numbers (called weights), stored on GPUs or TPUs.
Different types of generative AI use different architectures depending on the task:
Generative AI models go through a training process that is eerily similar to how humans acquire a new skill — multiple practice/feedback cycles.
1️⃣ An AI model first gets fed a huge amount of information: text for text-generation models and images for image-generation models.
2️⃣ It then analyzes the data for patterns — what words follow what words, where punctuation symbols are put, and so on.
3️⃣ The AI uses a math formula to commit these patterns to memory — this is called distributing the weights.
This is repeated millions of times, until the statistical patterns from the training data are reproduced reliably.
☝️OpenAI, for example, keeps the exact details of their training datasets secret, but we believe that they used 45TB of data to train the now depreciated GPT-3. This is an obscene amount of information, and we can safely assume that newer models (GPT-4.5, GPT-o3) infest even more during their training.
By the end, the AI will have acquired an ability to create content that looks like the examples it saw in the training dataset.
The most common types of models in use today are:
Below is an explanation of how each of them works.
GPT stands for Generative Pre-trained Transformer. This technology was invented by OpenAI. Today, it powers the most popular chatbot — ChatGPT, as well as its alternatives.
When you ask GPT a question it will use what it "learned" during the training phase to predict what words should come next. This way it can generate an output that looks like human writing.
Essentially, it’s a statistical prediction algorithm. And this is why GPT models can sometimes “hallucinate.”
💡GPT models don’t understand the concepts they’re describing — they’re just putting words together in a way that is likely to be correct.
Diffusion models use what’s called a refining process. Examples of these models are:
Diffusion models start with random noise and then turn it into an image.
For example, if you tell the model “Generate a black cat” it will first take black pixels and push and pull them around until they make a vague cat-shape. Next it will put the green pixels into where the eyes are — this way the image gets (a little) less noisy every time the model performs this rearrangement.
👆That explanation is grossly oversimplified, but it’s a good way to think about it.
👉 Most models take 20–1000 iterations. The more iterations, the better the end result.
After enough steps, the noise turns into a recognizable picture that matches a prompt.
GAN (Generative Adversarial Networks) actually use not one but two AI models.
Model A (generator) creates an image. Model B (discriminator) looks at the image and decides if it looks “real” or “fake.”
The generator first creates crude images — this is deliberately done to establish a baseline of what’s fake for the discriminator.
But with every step the generator makes something more coherent. Over many rounds of this, the generator gets good and its images start to look realistic.
What are GANs usually used for? Deepfakes — because they’re best for photorealism, and video generation, because, unlike denoising stable diffusion models, they create the final result on the first go.
These technologies are theoretically impressive, but what’s really cool is the wide range of tools they can power:
👉 Chatbots
👉 Image generators
👉 Audio Generators
The most famous example of this is, of course, ChatGPT. Chabotobs can answer questions, write in a way that’s reminiscent of human writing, even generate code at the level of a good developer.
People often use chatbots to automate their routine:
For example, one of the most popular tools on Overchat AI is a GPT-based homework helper. You send it a picture of your homework or paste it into the chatbot, and the AI solves it.
DALL-E 3, Midjourney, and Stable Diffusion can create images from prompts.
For instance, you can ask an AI to create "an icon in the style of Windows 11" or to "turn my selfie into a Ghibli illustration."
The results can be very realistic. Here’s a case where an AI art was submitted into a prestigious competition… and won first place.
What are the applications of image generators?
Try applying a Ghibli filter to your images for free in Overchat AI.
Let’s quickly recap what we’ve learned:
The three most popular types of AI models are:
GPTs are used in chatbots, diffusion models — in image generators, GANs — in video generators.
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