How Generative AI Models Work: A High-Level Overview

Post pandemic in 2020-21, the world has changed drastically. Today, AI is dominating all industries, whether it is technology, retail, e-commerce, or you name it. However, the most advanced field that is still evolving and evolving faster than any other is Generative AI. From powering text creation and music composition to generating realistic images and enabling smart product design, GenAI has come a long way.  

But today we will dive into how generative AI models work. What is the mechanism underlying the GenAI work? At the end of the JumpGrowth blog, we will know how businesses can use GenAI, especially for product development- all in a digestible, high-level guide. So, let’s start with the blog. 

Before we dive into what generative AI models are, you should know that worldwide GenAI spending is expected to total $644 billion in 2025, which is about a 76.4% increase compared to last year. Gartner 

What Are Generative AI Models? 

As its name indicates, generative AI is an algorithm capable of generating latest content for which you have code. The best examples of generative AI are ChatGPT, Sora, Veo 3, etc. These are the most famous and common generative AI examples. Apart from this, today we have generative AI for every task, whether we need to generate text, images, audio, video, or even code. If we compare generative AI with traditional AI models, they are too advanced and, in contrast to traditional models that classify or predict based on inputs, generative models generate novel outputs. 

If described the generative AI in a single phrase, “Think of machines that don’t just understand data but can also replicate it and can be creative with it.  

Understanding the Basics of Generative AI 

According to a survey between 115 to 180 million users worldwide uses generative AI every day, but when it comes to know how generative Ai works there are hardly 10% users.  

Generative AI works in a two-phase approach, not all but most of the GenAI models:  

1. Training Phase: Every GenAI model goes under the training phase where they are trained to provide an accurate and reliable output to your prompt. During this phase, developers train them with the vast datasets. AI models learn patterns, relationships, and distributions from a dataset (text, images, etc.). 
2. Generation Phase: This is when a user uses the GenAI models. During this phase, the users command the AI model, and the model uses what it has learned to generate new data samples that resemble the original dataset. 

Key Techniques in Generative AI 

As mentioned earlier AI models go under the training phase where they are trained with a huge amount of data. To memorize this data easily and retrieve it in no time AI models use a few techniques to learn from data and then create new things like text, images, or music. Below are those techniques.  

1. Learning on Its Own (Self-Learning): Most of the Generative AI can train or learn on their own, they do not need a teacher.  AI models look at the huge amount of data (you provided during the training phase) and figure out patterns by themselves. This technique is also called unsupervised or self-supervised learning. 
2. Guessing Using Probability: Another technique which is used to provide output is the guessing using probability. We all know what probability is, AI uses math and probabilities to guess what should come next. For example, if you type “The cat is on the…”, the AI will guess “mat” because it has seen that sentence many times before. 
3. Using Deep Learning (Like a Digital Brain): Inside these AI models are powerful systems called neural networks. These neural networks work a bit like a human brain. They help AI to understand complex data and create new content from it which accurately aligned with the user’s input. 
4. Creating a Hidden Map (Latent Space): Many AI users do not have any clue about the latent Space. So latent Space refers to a map which ai uses to mark the things which it has learned. This is a sitemap of any website from where it can retrieve the data easily in no time and provide the output as per your requirement.

Example: 

Imagine you have read 100 books on psychology and now you are at that level where you can publish your own book which will consist of knowledge from every book. Hence, you will not copy a single book, you will provide the knowledge based on 100 books you have read, and the output will be more enhanced based on what you have learned.  

How Generative AI Models Work 

Now that we know about Generative AI, what are the techniques to train it? The next thing that we will know is how generative AI works. So, the generative AI works in three core steps: 

• Input Feeding: Obviously the first and foremost thing for any AI model is it needs input. Any kind of input or seed such as text, noise, or prompts to start the generation process. 

• Latent Space Learning: Once you provide input to generative AI model, they create a latent space a compressed mathematical representation of the input data. The model then learns to navigate this Space to generate similar outputs and provide output to the users. 

• Sampling and Decoding: Once trained, the model can sample a point in the latent Space and use a decoder (like a neural network) to transform it into something meaningful, which can be a new sentence or image. 

Types of Generative Models in AI 

Several architectures and techniques are used in generative modeling. Here are the most widely used ones: 

• Generative Adversarial Networks (GANs): It was invented by Ian Goodfellow in 2014. It consists of a generator and discriminator and is used for image generation, deepfakes, and video synthesis. Its main strength is its capability to generate high-quality images.  

• Variational Autoencoders (VAEs): It is a type of deep leaning model used for unsupervised learning and data generation. It simply encodes data into a compressed form and decodes it back. VAE is considered more stable than GANs, but its outputs are often blurrier. It is used for structured and controlled data generation. 

• Autoregressive Models (like GPT): These models are statistical models capable of predicting future values based on their past values.  Today’s most famous example of autoregressive models is GPT-4, and ChatGPT. These models are fluent and considered best for context-aware text generation.  

• Diffusion Models: These are the models primarily used for creating high-quality images or other data by learning to reverse a diffusion process. Diffusion models are currently used in tools like DALL·E 2, Midjourney, and Stable Diffusion and are best for the extremely high-quality visuals. 

These are the different types of generative AI; however, GPT and diffusion models are the two most used models.  

How GPT and Diffusion Models Work

Generative AI uses several types of models to create things. Nowadays, GPT and Diffusion models are used the most. Let us understand how these two models work in a super simple way: 

GPT (Generative Pretrained Transformer) – Used for Text Like ChatGPT 

• GPT is like a very advanced autocomplete tool and is the fastest tool to reach a million users. GPT models keep going with what they have seen or trained in the past. You give it a few words or a prompt, and it predicts what comes next, one word at a time. GPT models are trained with millions of books, articles, websites, etc., and they know how sentences are usually written. ChatGPT is the best example of GPT models; it can write essays, emails, code, poems, and even jokes! 

Example: 

You type: “AI is changing the future of…” 

GPT continues: “…technology, healthcare, and education by making tasks easier and faster.” 

Hence, it keeps going based on what it has seen before and gives the output accordingly.  

Diffusion Models – Used for Images Like DALL·E or Midjourney 

In contrast to GPT, diffusion models work backwards from noise, from a blurry mess of pixels. To begin with, they add random noise into a clean image until it becomes chaotic (just like fuzzy TV screens). An AI is then trained to remove the noise and reconstruct the original image. Having learned to do this, the AI will acquire the ability to generate a completely new image from random noise. 

Example: 

You type: “A dog wearing sunglasses on a beach.” 

The AI starts with static (noise) and gradually creates a clear image that matches your prompt.

Main Difference: 

These two types of models are the backbone of most modern AI tools, whether you’re chatting with ChatGPT or generating an artwork on Midjourney. 

Generative AI for Product Development 

Today, Generative AI isn’t just a lab novelty anymore; it has become a practical tool for product development. It has totally changed the way product development is done. Below is how Generative AI is reshaping the product development industries.  

• Prototyping & Design: Prototyping and design is the most important part of product or app development. With the help of generative AI, you can generate hundreds of product designs. Along with this you can command AI models to customize output based on your customers preference.  

• Testing & Simulation: Testing and simulation are the next and most important parts. We all know developing a world-class product means nothing if it is full of bugs. With generative AI developers can easily detect any error or bugs in the development phase and correct them in no time. You can use AI to stress-test your product in a virtual environment before launch. 

• Creative Content Generation: Content creation is a hectic task for a developer. Fortunately, generative AI also eliminates this task, as with your help, you can write any type of content, from product descriptions, product menus, taglines, slogans, to ad copy. Also, you can easily use generative AI in marketing and identifying user behavior.  

Understanding Generative AI Models: Key Takeaways 

Conclusion

Generative AI has totally changed the way we create design and interact with technology. Today, there is hardly any field where professionals are not using generative AI to ease down process and reduce the chances of human error. Generative AI models offer immense potential whether it’s about enhancing creativity, automating workflows, or speeding up the development process.  

With the maturing of generative technologies, an early-adopting company will gain a very powerful competitive advantage using the custom AI and ML development services.  

Get connected with our experts to explore what generative AI can do for your business and help it to reach the pinnacle.