If you’re still running your business in the same way as last year, here’s a hard truth: you’re missing something important.

That’s Generative AI.

Today, businesses are using generative AI to make their product more appealing and solve problems smartly. While others make their teams work harder, some let AI do that work.

But behind this, there are different types of generative AI models. Each has its own uniqueness and creates something special. Some write like humans. Some design great images. Others create videos, compose music or even help developers write code faster.

This is the reason why startups, investors and leading businesses are rushing to use generative AI. It helps them to get the task done in minutes. New products can be tested quickly.

Let’s see what the main types of generative AI models are, how they work and their application in simple terms.

What are Generative AI Models?

Generative AI models are powerful software applications that create new tasks, like text, pictures, audio and even video by extracting patterns from a huge dataset. Usually, traditional AI follows pre-determined rules, while generative AI understands the basic patterns and uses the information to generate innovative content.

Think of it like using ChatGPT. Let’s say you’re the owner of a small e-commerce business and you want to have a product description. Instead of writing it by yourself, all you have to do is enter:

“Write a simple product description for “product name.”

Within a second, generative AI models produce a simple and understandable product description for your product.

Types of Generative AI Models

Generative AI is not a tool with a huge brain. It is made up of different types of models, and each generates the content in its unique manner. Just imagine them as different artists, like a few are amazing painters, others are great writers, some are musicians, designers. Let me explain easily:

Generative Adversarial Networks (GANs)

GANs work like a smart competition between two artificial intelligences. One of them generates fake images, while the other acts like an auditor, trying to detect the fakes. Each time the auditor finds the error, the creator receives feedback and makes the next image even better.

This process continues until the images are so realistic that they are harder to differentiate from the real ones.

The main applications of GANs are:

Generating ultra-realistic faces and landscapes
Enhancement of photos and design of visuals
Improve face generators and editing software

They are capable of producing stunning results, but sometimes, very complicated or odd scenes can confuse them.

Variational Autoencoders (VAE)

VAEs are the process of reducing the amount of data and then reconstructing it. In this way, they extract the most important features of the data. When they have this "pattern", they can produce new, similar content by themselves.

It's similar to learning a basic recipe and then creating multiple versions of the same meal.

VAEs commonly participate in the following activities:

New designs and variations creation
Medical images enhancement and analysis
3D model construction
Working with missed or unclear data

The results are typically very smooth and consistent, even though they're not always highly detailed.

Autoregressive Models

Autoregressive models produce the content simultaneously, in a step-by-step process. So, the next word, note, or element is determined by everything that happened before.

It's like writing a sentence word by word, or composing music note by note. The model observes the already generated pattern and predicts what will come next.

These models are suitable for:

Novel and article writing
Chatbots and writing support
Sentence completion and text prediction

They can maintain a natural flow of content, but with very lengthy texts, sometimes they just lose track.

Transformer-Based Models

Transformers have become very powerful and popular models in their respective fields. The attention mechanism is what makes them special. This is a system that allows them to detect the important parts of the information they are processing, even if these parts are separated far apart in a single sentence or even in a multiple-page document.

It's like how humans pay attention during a discussion, focusing on what's important while recalling past details.

Transformers are found in:

Writing tools (like text generators) of the latest generation
Translation applications
Text-to-image generation tools
Chatbots and virtual assistants

A model often performs better the larger it is and the more data it has been trained on.

Diffusion Models

Diffusion models are one of the most innovative ways of generating tasks. Starting from a random noise (similar to TV static), they keep on gradually removing it until at last a picture gets revealed.

The process is like watching a blurry image that slowly turns into a clear image.

Diffusion models are especially related to:

Producing highly detailed and realistic AI artworks
Giving the users the power to control style and details
Being the backbone of leading AI image makers

They do require powerful hardware, though the outcome shows the most impressive AI-assisted image generation.

Recurrent Neural Networks (RNNs) and LSTMs

RNNs and LSTMs represent the older generation of models, but they are still very much in demand whenever working with sequential data. This includes any type of data that follows an order, such as text or sound.

RNNs have a limited amount of “memory,” which enables them to keep track of the previous inputs, while LSTMs are able to store such information for even longer intervals due to their advanced architecture.

They are used in:

Virtual assistants
Composition of music
Speech recognition
Applying some written-based tools

Even though Transformers are already used in many modern systems, RNNs and LSTMs are still useful for certain tasks.

Real-World Business Use Cases of Generative AI

Generative AI has become a daily integrated operation of real businesses in the background. It is being implemented in various fields, ranging from small startups to multinational corporations, to save time, money, and user satisfaction through better experiences.

Software Development

Generative AI models help by writing the code, fixing bugs, testing the apps, and even making the technical documents for the software. It allows the developers to build and maintain the products at a higher speed than before.

Customer Service

AI-based chatbots in customer service provide answers to the clients' queries 24/7, just like a human. They perform tasks like summarizing support tickets, drafting follow-up emails, and creating help articles, which together improve the speed and support for all parties involved.

Education

Teachers and students in the educational sector use AI to help them in making and organizing study materials, reviewing the teachings, and getting instant tutoring assistance. It cuts down teachers' and students' time and effort, and turns learning into a more personalized experience.

Finance and Banking

The AI algorithms used in finance and banking to analyse market trends, write reports, and spot fraud through the detection of unusual behaviour. This clears the way for the deployment of smarter decision-making as well as secure systems.

Design and Media

In the field of design and media, AI's capabilities go as far as creating images and videos without the use of cameras or studios. The marketing teams take advantage of this by creating posts, ads, and even professional profile pictures in seconds.

Healthcare

Generative AI takes patient records and summarizes them, analyzes medical scans, and then helps doctors in making decisions and supports new drug research. AI also takes care of the billing and paperwork, and appointment reminders.

Product Development and Management

AI helps to generate ideas, design, problem prediction, and planning of work in the field of product development and project management. In sales and marketing, one can write the content, find the right audience, and customize the messages.

How to Choose the Right Type of Generative AI Models?

Selecting the best generative AI model is not as difficult as it might seem. In the end, everything comes down to three basic questions: What would you like to create? How much control do you need? And what resources do you have? Once you’re clear on that, the decision becomes a lot easier.

If your main purpose is to create pictures, designs, or artworks:

Diffusion models are most suited for images that require a lot of detail and high quality.
GANs are great for realistic pictures and output ready in very little time.
VAEs are useful to regulate specific details of the picture

If you want to create text-related stuff such as articles, emails, and code:

Transformer models are the most effective solution for most writing tasks.
Autoregressive models are good for structured text, such as code.
RNNs/LSTMs can take care of simple text tasks with less power consumption.

For sound and voice generation

Diffusion models have the highest standards for music and speech.
Autoregressive models perform music composition gradually.
GANs are capable of modifying or imitating voice styles.

If the purpose is videos or 3D content, then:

Diffusion models are leading the way in video creation
GANs are the best options for adding motion and face effects.
Transformers are also gaining traction in video processing.

In simple terms, just adapt the model to your aim. Want a text? Use a text-focused model. Want pictures? Pick an image model. Want music or a video? Choose models designed for that.

The choice is easy if your objective is clear.

Conclusion

At the time you’re waiting to use generative AI, your competitors will move one step forward.

Behind all the advanced pictures, the quick-written articles, the AI-generated videos, or the lines of code is a strong generative AI model that is working silently in the background.

And now, you know that it is not just one model. There is an entire system of various types, each specifically designed for creating something unique from texts and images to noises and complete experiences.

The good news? You don’t need to be a technical expert to use it. All you need is to hire a generative AI development company, which helps you to build an engine that completes all the tasks in just a minute.

That’s where Hashcodex comes in.

We handle everything from choosing the right AI model to designing, developing and deploying it. Our team develops a solution that is customized to your goals, your sector, and your budget, whether you want to produce content, automate assistance, create images, analyze data, or create a unique AI-powered product.

Connect with us today!