Generative AI Models

Written by Coursera Staff • Updated on

Learn about the different types of generative AI models and their varying capabilities as you decide which one to implement.

[Featured Image} A group of four tech professionals look at AI generated photos on a laptop and discuss generative AI models.

Generative AI is a branch of the artificial intelligence industry dedicated to responding to your prompt or question with unique, system-generated content. AI encompasses several different fields, and AI researchers have been broadening its reach. While generative AI is only one section of a much larger industry, it uses different types of models to function, such as Large Language Models, known as LLM, image generators, code generators, and audio generators. All of these models generate a form of content that you can use and apply to your work or learning environment.

Types of generative AI models

As a type of machine learning model, generative AI uses trial and error to generate new content, and it does so through a number of different models. Generative AI models learn to develop content similar to what they have previously seen regarding data. The goal is to achieve unique content that accomplishes what you asked it to do.

Generative AI models have rapidly progressed to the capability of creating text, images, videos, and audio for use in daily life. When deciding which generative AI model to use, you should first consider what you are trying to accomplish, whether it be automating business processes, working on an assignment, developing learning materials, or fixing computer code.

Large language models

LLMs, large language models, can mimic the human language by compiling data from books, articles, and online sites to generate unique combinations of text. By detecting patterns across a wide range of information, LLMs can create content that is consistent with human language in a creative manner. LLMs are changing how we communicate with computers. Initially able to predict the next word in a sentence, LLMs can now write full pages of text from a simple prompt.

GPT-4 is a popular LLM and AI software designed by OpenAI. ChatGPT is a specific product of GPT-4 that can mimic human tone and respond to prompts with long- or short-form writing materials. GPT-4 programs can answer questions, summarize texts, create captions, and even write full-scale essays. By learning from their own outputs, LLMs can improve themselves over time.


The advantages of LLMs include their ability to automate business and learning processes. By developing text at a rapid speed unmatched by humans, LLMs can complete administrative-centered tasks, allowing you to focus on more creative opportunities. Apart from changing how we communicate with and utilize technology, LLM models can develop more accessible learning tools for students with captioning, text-to-speech, and other educational capabilities. ChatGPT is just one example of the possibilities of Large Language Models.


While LLMs can improve our communication, they have drawbacks as well. Because of its pattern recognition process, LLMs like GPT-4 can generate content that is factually inaccurate and untrue. LLMs predict likely words in sentences and writing and do not possess the ability to sort through their writing for false statements. Additionally, LLMs’ usage of largely unfiltered information from the internet means it could also be untrue and offensive. Relying solely on software like ChatGPT could result in academic dishonesty, factually inaccurate statements, and writing that could contain racist, sexist, or homophobic language.

Image generators

Image generators are a type of generative AI model focused on creating works of art or visual images from a written prompt. Like LLMs, image generators analyze patterns in art available online, recreating works of art generated from combinations of visual data. Image generators cannot create entirely creative works, as everything they generate is a combination of other images available online, mainly from the creativity of humans.

For example, providing an image generator like DALL-E with a text description allows it to create realistic images based on the concepts and styles. It does so by analyzing artistic patterns and applying them. However, if you ask DALL-E to generate an abstract work of art, it will not create anything explicitly creative. Instead, it will develop the image based on patterns connected to the idea of abstract art.


Image generators can be a great tool to instantly develop a complex image idea. In addition to entertainment, image generators provide a new way of developing visual aids, architectural plans, and art models. By creating realistic visual art, image generators can assist artists and architects in brainstorming new ideas and completing their work at a faster pace. If you are working in the graphic design industry, you can collaborate with image generator AI tools to refine and re-work different ideas and design plans.


Because image generators compile visual data from the internet to craft new ideas, they often supply you with copyrighted or plagiarized work. You should be careful when applying the images from image generators into work or learning environments, as you could be perpetuating copyrighted work. Image generators can also cause harm to others by generating what’s known as “deepfake” videos and images. Deepfakes can depict real people doing whatever you prompt the machine to create. Image generators can also create images that are racially or sexually biased, as they are dependent on the information available on the internet, which can often consist of offensive imagery.

Audio generators

Audio generators encompass text-to-speech, speech-to-text, and music or audio creation tools that can help with learning and creative purposes. With speech-to-text tools like, you can transcribe messages you speak aloud or even create transcripts for lesson plans in classrooms. Text-to-speech tools do the opposite; you can write prompts that will speak aloud for you or others, allowing you to hear your own words read aloud to you or have messages from others dictated to you. Music and audio generative tools now will enable you to generate new recordings of pre-existing songs that anyone can sing.


Audio generators provide a new level of accessibility that cannot go unnoticed. Speech-to-text and text-to-speech tools can go a long way in restoring the ability to communicate for those unable to speak or see. Audio-generative AI can also develop voices for those unable to speak, meaning you could communicate with someone who is mute. Audio generators also provide more dynamic learning materials for students, allowing them to both see and hear the material.


Similar to image and language-generative AI, audio-generative AI is at risk of utilizing copyrighted material to create content. It can essentially take people’s voices and make them say whatever prompt you type into the software. This ability opens the door to invading people’s privacy, not to mention the impacts that AI-generated audio could have on the music industry.

Code generators

Code generators are a form of generative AI that can create code using machine learning algorithms and models. It may analyze industry standards and best practices and leverage natural language descriptions of the type of code you require. Google has a form of code-generative AI called Bard, which works with over 20 different programming languages to help create or aid you in completing code sequences.


Since code is both verifiable and testable, you can test code generated through AI for accuracy in a way that image, audio, and textual generative AI cannot. It can identify and fix bugs, enhancing the quality of your code. If you are a developer, code-generative AI can help you reach your goals and deadlines faster and provide you with access to various AI-generated solutions you can choose from and refine.


Code-generative AI can be challenging because code is a diverse type of content requiring precise outputs that may only sometimes be feasible with AI. Code generators can also replace humans in some positions in the future, potentially disrupting the job market worldwide. They need more user control, which can result in security liabilities, and they require a large amount of power, which can sometimes make your job more difficult if you use this type of AI.

What are generative AI models used for?

Generative AI models' primary purpose is creating new content, but you should proceed cautiously. Generative AI can supplement human work. However, it’s imperative that users refrain from using AI-generated content and claiming it as their own work. For day-to-day administrative tasks in almost any industry, you can use generative AI models to formulate spreadsheets, translate languages, synthesize information, and summarize documents to enhance your capabilities. AI improves productivity by automating business processes and making materials accessible to various learners.

Who uses generative AI models?

Generative AI models are prevalent in a wide variety of industries. Often seen in the educational sector, many institutions are coming to terms with learner use of AI. You are not alone if you are a learner using AI to assist you with assignments, homework, or projects. Ethical issues arise when learners use AI as a replacement for their own efforts rather than using it as a collaborative tool to work through writer’s block or help them brainstorm ideas.

Faculty often use generative AI to help develop lesson plans and accelerate their teaching strategies. Generative AI models are also prominent in fields like marketing or graphic design/development, as you can use generative AI to create visual and textual content for market segmentation or user personalization, as well as code for digital companies.

How to get started in generative AI models

Learning how to approach AI as a collaborative tool is an essential starting point as you build your knowledge and skills in generative AI. Understanding the technology is only one part of the equation. Using AI to your best advantage means boosting your capabilities and creativity through digital technology while preserving your integrity and acting ethically, whether in school or in your professional life.

In other words, generative AI models should not be the sole proprietor of your content; you should let your individuality take precedence and use generative AI models to enhance your ideas. If you understand that you should not solely rely on AI and that creating a prompt for a generative AI model is doing the majority of the work, then you can feel comfortable using AI as a collaborative digital tool instead of one that can do everything.

Getting started with Coursera

Learning about generative AI models and how and when to use them is an important skill if you are working in a digitally-centered industry. On Coursera, you can improve your technological and artificial intelligence literacy through courses like Generative AI with Large Language Models. This course will provide instructions from top AI practitioners and introduce you to the fundamentals of the field and where AI research is heading.

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