Google Gemini AI: Meaning, Capabilities, and Use Cases

Written by Coursera Staff • Updated on

Learn more about Google Gemini and what its suite of large language models can do.

Google touts Gemini as the next big thing in generative AI: a suite of multimodal AI models designed to work across various devices and systems of varying sizes. But what is Gemini AI exactly and what can you reasonably expect it to be able to do in the not-so-distant future? 

In this article, you’ll learn more about Gemini, including the different generative AI models it encompasses, what they can do, and the benefits they may provide organizations. At the end, you’ll even explore online courses developed by the industry leaders at Google that can help you build foundational knowledge of generative AI today. 

What is Google Gemini? 

Gemini is a suite of generative AI models created by Google to power a range of different digital products and services, including their already available Bard chatbot and several other yet-to-be-revealed projects. Positioned as a direct competitor to OpenAI’s GPT models, Gemini consists of three different large-language models (LLMs) of varying size and complexity that use natural language processing (NLP) to dynamically interpret and respond to user inputs. 

Gemini’s models are examples of “multimodal AI models,” meaning that they can respond to a range of content types, such as text, video, audio, and programming code. As a result, Gemini models can theoretically perform many different tasks like interpreting the notes on a music sheet, combining images to create new ones, or quickly generating a piece of writing. 

Much like OpenAI’s GPT models, however, Google’s Gemini models may not always perform certain tasks reliably or accurately. In effect, although the technology may enable countless possibilities in the future as new iterations are created, it’s important for individuals to temper their expectations about what this still-developing technology can do today and assess the quality and veracity of their outputs on a case-by-case basis. 


Gemini AI includes three different models, which vary in size and intended use. These models include:

  • Gemini Ultra: Gemini’s largest model, created to accomplish the most complicated tasks.  

  • Gemini Pro: Gemini’s most scalable model, capable of performing a wide range of different tasks. 

  • Gemini Nano: Gemini’s most efficient model, specifically designed for on-device tasks. 

Currently, Google has not revealed the precise tasks each model can perform. However, the company is expected to make this information available in the near future. 

Google Gemini capabilities 

Gemini models are multimodal, meaning they can interpret and respond to various types of content, including text, video, audio, and code. This means that Gemini models can theoretically perform a wide range of tasks, such as writing code for an application, generating images, or composing text (among many other things). As a result, the precise ways that Google and other organizations might implement Gemini models will vary based on their overall goals and objectives.  

In an aspirational demo video of what interactions with Gemini models may look like one day, a user is shown drawing a picture on a piece of paper that Gemini accurately identifies as representing a duck. Afterward, the AI notes how to say “duck” in several other languages, creates and plays interactive games with the user, generates images of what the user can make with two balls of yarn, and directly responds to images from videos with interpretations of them. 

Although these feats are impressive, it’s important to note that the video showcases what interactions with Gemini-powered AI may look like in the future rather than what they’re like right now. Much like other LLMs, such as those used to power OpenAI’s ChatGPT, Gemini’s models are expected to become more capable as new advances are made in the coming months and years. 

Potential benefits 

There are many potential benefits to generative AI. According to one 2023 study by researchers from Harvard, UPenn, MIT, and the Warwick Business School, generative AI can improve the performance of highly skilled workers by as much as 40 percent when used to complete certain tasks [1].

Another 2023 report by McKinsey & Company, meanwhile, asserts that generative AI’s “impact on productivity could add trillions of dollars in value to the global economy” as the technology is used to automate work tasks that “absorb 60 to 70 percent of employees’ time today” [2]. 

Ultimately, many researchers emphasize the ability of generative AI to help employers reduce costs, increase efficiency, and improve overall productivity. 

Build your generative AI skills today

Generative AI is poised to radically transform how many businesses operate and how employees do their work. Prepare for this new work world by taking a flexible, online generative AI course or specialization on Coursera today. 

In Google’s Introduction to Generative AI microlearning course, you’ll learn what generative AI is, how it’s used, how it differs from traditional machine learning methods, and explore the Google Tools available to start developing your own generative AI apps. In just one hour, you’ll gain a foundational understanding of generative AI from the experts at Google themselves. 

In Vanderbilt’s Prompt Engineering Specialization, you’ll learn how to tap into the emerging capabilities of large language models to automate tasks, increase productivity, and augment human intelligence. You’ll gain skills to apply these tools in your work, education, and daily life through practical hands-on courses.

Article sources


MIT Sloan. “How generative AI can boost highly skilled workers’ productivity,” Accessed January 11, 2024

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