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February 21, 2024

Google Gemini: How Can It Be Used?


Artur Haponik

CEO & Co-Founder

Reading time:

9 minutes

Google is a major player in the tech sector. Therefore, it comes as no surprise that it has invested significant resources in creating some of the most advanced AI models on the market right now. A product of Google’s DarkMind and Research, Google Gemini has emerged as a promising generative AI model with next-generation capabilities and state-of-the-art performance. Despite only being in the market a little over two months at the time of this publication, Gemini has already garnered significant interest among developers and AI enthusiasts.

In this post, we will explore the reasons behind Gemini’s increasing popularity, with a keen focus on what it can do and how to use it correctly for optimum results.

What is Google Gemini?

Google Gemini is a family of generative AI models. Unlike previous LLMs like OpenAI’s ChatGPT, Google’s tool can understand, process, analyze, and generate different data types, including text, images, and videos, making it a multimodal AI model.

Read more: Google Gemini API vs. Open AI API: Main Differences

The models are a product of collaborative large-scale efforts by teams across Google, including DarkMind and Google Research. As it stands, Gemini is currently available through integrations with Google Bard and Pixel 8. According to the company, Gemini will be gradually incorporated into other Google services, including Google Search and the Chrome browser.

This tool will be available in three sizes, including:

Gemini Nano

The Nano model is specially designed to be used on smartphones, particularly the Google Pixel 8. According to Google representatives, Gemini Nano is designed to work on-device tasks that don’t require a dedicated connection to external servers, like summarizing text and suggesting replies in chat applications.

Gemini Pro

Unlike the Nano model, Pro runs on Google’s data centers and is designed to power the latest version of Google Bard, the company’s latest AI chatbot. According to the company, Gemini Pro is capable of understanding complex queries and delivering fast response times.

Gemini Ultra

Google Ultra is taunted to be the most capable model of the three, exceeding current state-of-the-art results in 30 out of the 32 most widely used academic bookmarks used in LLM research and development. Although it’s not currently available for widespread use, Ultra is capable of performing highly complex tasks. According to Google representatives, the model will be released after completing its current testing phase.

Google Gemini Models Size, gemini pro

What makes Google Gemini so popular?

Despite being a frontrunner in AI research over the past decade and developing the transformer architecture, that powers most Large Language Models (LLMs), Google is still slacking behind OpenAI with its generative AI GPT models.

The Gemini Models are Google’s attempt to play catch-up. The modes’ multimodal capabilities are sure to give the company’s competitors a run for their money, but that’s not all they have to offer.

Here’s why Google’s AI may soon become one of the most popular generative AI models on the market.

State-of-Art Performance

According to Google, the company has been rigorously testing and evaluating its model’s performance on a wide variety of tasks, including natural audio, image, and video understanding and mathematical reasoning.

Gemini UItra’s performance, for instance, has exceeded current state-of-the-art results on 30 of the 32 most widely-used academic bookmarks used in LLM research and development, earning an aggregate score of 90.0% [1].

This makes Gemini Ultra the first generative model to outperform human experts on MMMU (massive multitask language understanding), which utilizes a combination of 57 subjects, including physics, math, history, law, ethics, and medicine to test generative models’ problem-solving capabilities and world knowledge. [2]

Google Gemini also achieves a state-of-the-art score of 59.4% on the new MMMU benchmark, which consists of various multimodal tests spanning multiple domains that require deliberate reasoning. In the image benchmarks, Gemini Ultra outperformed previous state-of-the-art models by utilizing an Optical Character Recognition system that extracts text from images for further processing and analysis.

Next-Generation Capabilities

Unlike most multimodal models, which are trained using the standard approach of training separate components for different modalities and then stitching them together to roughly mimic multimodal functionality, Gemini was designed to be natively multimodal.

While models trained on the standard approach can perform relatively well on certain tasks like describing images, they often struggle with more conceptual and complex reasoning. Google Gemini, on the other hand, is pre-trained from scratch on different modalities and then fine-tuned with additional multimodal data to further refine its accuracy and effectiveness.

This approach helps the Gemini models to seamlessly understand, process, and reason different types of inputs, making it more reliable than existing multimodal models.

Read more: Multimodal Models: Integrating Text, Image, and Sound in AI

Some of its most notable next-generation capabilities include:

Sophisticated Reasoning

The Gemini Models’ sophisticated reasoning capabilities can help them make sense of complex visual and written information. This makes them great at uncovering knowledge that couldn’t be possible to discern when dealing with vast amounts of data.

This remarkable ability to extract insights from vast amounts of data through analyzing, filtering, and understanding information will ultimately help deliver new breakthroughs at digital speeds in multiple fields, such as science and finance.

Advanced Coding

Most LLMs on the market, including OpenAI’s GPT models, can generate code. However, Gemini takes this up a notch with its remarkable ability to understand, explain, and generate code in multiple programming languages including Java, Python, C++, and GO.

It also excels in various programming benchmarks, including HumanEval, an industry-standard benchmark for evaluating performance in coding tasks. [3] It achieves this by utilizing Google’s internal held-out dataset, Natural2Code, which uses author-generated sources in place of web-based sources.

Additionally, it can also be used to create more advanced coding systems. For instance, Google recently used Gemini to create AlphaCode2, a more advanced model of AlphaCode, which is capable of solving complex coding problems involving complex math and theoretical science problems.

When evaluated against AlphaCode, AlphaCode 2 shows significant improvements over the previous model by solving twice as many problems. In fact, it is estimated that AlphaCiode 2 performs better than 85% of similar competitive models, representing a 35% increase compared to AlphaCode’s 50% score. [4]

How to access Gemini AI

Not all models are available to the public. However, you can access Gemini Pro, Google’s middle-tier model, via Google Bard and the Google Pixel 8 smartphone. There’s also speculation that it may soon be available on the web at and on mobile apps.

Accessing Gemini on Mobile Device

If you have an Android device, you can download the Gemini App from the Google Play Store or opt for an upgrade on Google Assistant. If you choose to go with the latter, you will be able to call it up just as you would with Google Assistant. This means that you’ll only need to press the power button or say “Hello Google” to use Pro.

Similar to what you’d expect with Google Assistant, it will pop out on your screen, where you can use voice commands to ask questions or give instructions on performing different tasks on your phone, such as generating a caption for a photo or summarizing text.

Accessing Gemini Through API

You can try out the Gemini AI API by signing up for a Gemini advanced subscription. [5] Alternatively, you can access this tool through a cloud-based API, which enables you to run Gemini in your applications.

To use the API, you first need to create an account and obtain an API key. The API keys are currently free, but we cannot rule out the possibility of paid subscriptions in the near future.

Once you obtain an API key, you can use it to call the Gemini AI API, which allows you to interact with Gemini and utilize its impressive capabilities.

Here’s how to get started with this tool:

  1. Visit the Gemini AI website and create an account
  2. Obtain an API key
  3. Install the client library for your preferred programming language
  4. In your code editor, import the Gemini AI client library and initialize it with your API key
  5. Call the Gemini AI to analyze images, generate text, answer questions, generate creative content, and much more

Tips for using Gemini AI effectively

Like with other Large Language Models, you need to utilize proper techniques to come up with the best prompts for proper results. In that regard, here are some tips on mastering the art of prompt engineering with Gemini AI:

  • Clarify your intention
  • Establish a persona (tell the Gemini AI who it is supposed to be)
  • Set the tone for your preferred output
  • Define the structure of the output
  • Use descriptive language in your prompts

Final thoughts

Google has been at the forefront of developing cutting-edge AI technologies. Therefore, it comes as no surprise that its latest release is giving its competitors a run for their money. Despite being relatively new and not fully available to the public, Gemini AI has caught the interest of developers and tech enthusiasts alike who are interested in trying out its potential capabilities.

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As it stands, you can access Gemini AI through Google Bard integrations on the Pixel 8 smartphone and through Gemini AI APIs. When utilizing the model, it is advisable to provide clear prompts with detailed instructions for accurate and relevant outputs.


[1] Rowancheung. URL: Accessed on February 16, 2024
[2] Measuring Massive Multitask Language Understanding. URL: Accessed on February 16, 2024
[3] Evaluating Large Language Models Trained on Code. URL: Accessed on February 16, 2024
[4] Storage. AlphaCode 2 Technical Report. URL: Accessed on February 17, 2024
[5] Developers. Gemini 1.5: Our next-generation model, now available for Private Preview in Google AI Studio
URL:,partners%20in%20Google%20AI%20Studio. Accessed on February 17, 2024



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