Generative AI definition: How it works, use cases and examples

Generative AI refers to a form of Artificial Intelligence (AI) that enables you to create content automatically. Using sophisticated algorithms and Machine Learning methods, generative AI can produce various types of media, such as images, text, videos and music in a matter of seconds.

Generative AI operates by analysing extensive datasets and generating content that mirrors the patterns found within the input data.

Today, it’s being used in applications across various sectors, ranging from artistic endeavours to practical tasks like content creation and storytelling.

At the front end, as a user, you provide input data (for example, you ask a question or ask it to create an image) to the generative model. It then uses this information to create output that's similar to, but not directly copied from the input. This process is iterative, where the model continually refines its output based on feedback and adjustments.

On the back end, deep learning plays a vital role in generative AI. Deep learning involves training neural networks to recognise and extract features from data.

Neural network learn to recognise patterns from data by adjusting their internal parameters to minimise prediction errors. For instance, you can train neural networks to recognise animals from images in a dataset. Once trained, the neural network can generate new animal images based on learned patterns.

Depending on the type of content they generate, different generative AI models work differently. For instance, text generation models use recurrent neural networks to predict the next word based on input text. Whereas, image generators, such as Generative Adversarial Networks (GANs), use two competing neural networks to produce realistic images.

Generative AI technology has become more accessible and is now being used across various industries. 👇

1 - Content creation

Generative AI can help you in creating different types of creative content like text, images, videos‌ and music.

This AI-generated content can be used for your marketing efforts, social media posts, storytelling‌ and entertainment purposes.

2 - Software development

Generative AI can also be used to generate code, automate software development tasks‌ and help in the creation of software applications.

This can streamline your development processes, reduce time-to-market‌ and improve the quality of your software products.

3 - Design assistance

Generative AI enables you to generate unique designs for your products, artwork, fashion items‌ and architectural plans.

It can help you explore new ideas, create prototypes‌ and optimise designs based on your preferences and requirements.

4 - Autonomous systems

Generative AI contributes to the development of autonomous systems such as self-driving cars, drones, robots‌ and intelligent agents.

It helps these systems perceive their environment, make decisions‌ and take actions based on real-time data and predictive models.

5 - Natural language processing

With generative AI, you can develop applications like chatbots, virtual assistants, language translation tools‌ and text summarisation programs.

These applications improve communication, information retrieval‌ and automation in your business processes.

6 - Simulation and modelling

Generative AI can be applied to create realistic simulations and models for your research, engineering projects, healthcare initiatives‌ and training programmes.

It enables you to simulate complex scenarios, predict outcomes‌ and optimise processes in various domains.

Today, there are many generative AI examples, used for all kinds of purposes.

• Software development - Natasha

• Text generation - OpenAI’s ChatGPT, Bard (Google AI)

• Image generation - Dall-E

• Music generation - Jukebox

• Video generation - Vyond, Sora

• Healthcare - Ada, Hyro

Natasha leverages cutting-edge expertise and the latest generative AI technology, allowing her to: 👇

1 - Understand how people will use this feature

Using generative AI, Natasha creates preparative elements that enable us to create software.

For instance, Natasha generates feature notes to help you describe what you want to achieve with a specific feature.

She also automatically generates high-quality user stories. These describe features from a user’s point of view ’As a [persona], I [want to], [so that]’ (for example, ‘As a shopper I want to invite my friends, so we both get a discount’).

She also creates acceptance criteria and test cases so it’s clear whether the feature will do what you want it to in different situations (what happens if someone doesn’t add an email during login for example?) And, at the same time, generates an implementation plan for each user story, which help us to create software.

2 - Generate code

Other than generating artefacts, Natasha also generates ‌software code. For the codegen process, Natasha first creates a code branch for the generator to work on and then generates the code in only under a few minutes.

This makes the code generation process much faster than what a human developer is capable of.

After generating the code, Natasha moves on to testing it. (She’s already generated the test cases so she knows how to do this and what good looks like.) Only after confirming that everything works the way it should, Natasha inserts the generated code inside the Builder.ai library.

In conclusion, Natasha uses generative AI to:

Understand customer ideas and needs → Identify necessary features → Convert them into user stories → Generate code