Gemini AI: User Guide with Instructions

Large language models (LLMs) are gaining popularity today. They are capable of generating not only text but also many other types of content: code, images, video, and audio.

Major companies, having large resources, train their models on text data collected by humanity throughout its history. Naturally, the international IT giant Google is no exception: it not only created its own model, Gemini, but also integrated it into its ecosystem of services.

This article will discuss the large language model Gemini, its features, and capabilities.

Overview of Gemini

Gemini is a family of multimodal large language models (LLMs), launched by Google DeepMind in December 2023. Before that, the company used other models: PaLM and LaMDA.

As of today, Gemini is one of the most powerful and flexible LLM neural networks, capable of conducting complex dialogues, planning multitasking scenarios, and working with any type of data, from text to video.

Capabilities of Gemini

The Gemini model not only generates content but also provides many additional functions and broad capabilities for working with different types of content:

• Multimodality. Through interaction with auxiliary models (Imagen and Veo), Gemini can work with different types of content: text, code, documents, images, audio, and video.

• Large context window. On paid plans, Gemini can analyze up to 1 million tokens in a single session. This is approximately one hour of video or 30,000 pages of text.

• AI agents. With some built-in functions, Gemini can autonomously perform chains of actions to search for information in external sources: third-party sites or documents in Google Drive.

• Integration with services. On paid subscription plans, Gemini integrates with services from the Google ecosystem: Gmail, Docs, Search, and many others.

• Special API. With the API provided by the Google Cloud platform, Gemini can be integrated into applications developed by third parties.

With this set of features, Gemini can be used without limitations. It serves as a universal platform both for end users who need content generation or specific information, and for developers who want to integrate a powerful multimodal AI into their applications.

How to Use Gemini AI

As part of the Google ecosystem, the Gemini model has many touchpoints with the user. It is available in several places: from search results in the browser to office applications on mobile devices.

So technically, you can access Google Gemini AI through various interfaces; all of them are merely “windows” into the central core.

Google Search Results

You can see Gemini at work in Google search results: the system supplements the list of found sites with additional reference information generated by Gemini. However, this doesn’t always happen.

In Google, this feature is called Generative AI Snippet. Gemini analyzes the query, gathers information, and displays a short answer below the search box.

Often, such a snippet turns out to be very useful. It provides a brief summary of the topic of interest. Thus, Google search results allow you to obtain information on a certain subject without going to websites.

Web Application

The most common and professional tool for interacting with Gemini is a dedicated website with a chatbot designed for direct dialogues with the model. This is where all the main Gemini features are available.

With such dialogues, you can communicate, create text, write code, and generate images and videos.

The Gemini web application has an interface typical of most LLM services: in the center is the chat with the model, at the bottom is a text input field with an option to attach files, and on the left is a list of started dialogues.

The interaction algorithm with the model is simple. The user enters a query, and the model generates a response within a few seconds. The type of response can be anything: a story, recipe, poem, reference, code, image, or video.

Yes, Gemini can generate images and videos using other models developed by Google:

• Imagen. A diffusion model for generating photorealistic images from text descriptions (text-to-image), notable for its high level of detail and realism.

• Veo. An advanced model for generating cinematic videos from text descriptions (text-to-video) or other images (image-to-video), notable for its high level of coherence and dynamics.

Thanks to such integration, you can enter text prompts for generating images and videos directly inside the chatbot. Quick and convenient!

The web version contains a wide range of tools for professional content generation and information gathering:

• Deep Research. A specialized mode for conducting in-depth, multi-step research using information from publicly available internet sources. With intelligent agents, Gemini autonomously searches, reads, analyzes, and synthesizes information from hundreds or even thousands of sources, ultimately producing a full report on the topic of interest. Unlike regular search, which provides short answers and links, Deep Research mode generates detailed reports by analyzing and summarizing information. However, one should understand that such deep analysis takes time, on average, from 5 to 15 minutes.

• Canvas. An interactive workspace that allows users to create, edit, and refine documents, code, and other materials in real time. Essentially, it is a kind of virtual “whiteboard” for more dynamic interaction with the language model.

Thus, Canvas is focused on interactive creation, editing, and real-time content collaboration, while Deep Research is aimed at collecting and synthesizing information to provide comprehensive reports.

Users can attach various files to their messages, from documents to images. Along with a text prompt, Gemini can analyze media files, describing their content.

Thus, the user can create multimodal queries consisting of both text and media simultaneously. This approach increases the accuracy of responses and creates a wider communication channel between humans and AI.

In other words, the browser version is the main way to use Gemini.

It is also worth briefly discussing how to register for Gemini and what is required for this.

In most LLM services, authorization is required. Gemini is no exception. To launch the chatbot, you must sign in with a Google account.

The registration process is standard. You need to provide your first and last name, phone number, and desired nickname. After this, you can use not only Gemini but also the rest of the Google ecosystem applications.

Mobile App for Android and iOS

You can download the official Gemini mobile app from Google Play or App Store. Functionality-wise, it is not very different from the web version available in a browser, but it has deeper features for user interaction and smartphone integration. Moreover, on many Android devices, the app comes pre-installed.

Essentially, it is a mobile client that expands cross-platform access to the Gemini language model. The main differences lie in optimization for specific platforms:

• Content management. On the browser version accessed from a computer, it is much more convenient to work with text, code, tables, graphs, diagrams, images, and video. Conversely, the mobile app interface, designed for touch and gesture interaction, simplifies use on smartphones and tablets, but does not offer the same efficiency as a keyboard and mouse.

• Voice input and interaction. The mobile app has more advanced voice input and live interaction features (Gemini Live), allowing you to communicate with the model in real time, using the camera to show objects, the microphone for direct conversation, and screen capture to share images. The browser version lacks this functionality.

• Device-specific features. The Gemini mobile app integrates closely with smartphone functions (clock, alarm, calendar, documents) for more personalized interaction. The browser version exists in a kind of vacuum and knows almost nothing about the user’s computer. Apart from accessing other websites, it has no “window” into the outside world. In rare cases, it can extract data from other Google services such as Gmail and Google Docs.

• Multitasking convenience. On a large computer screen, it is easier to work with multiple windows, copy and paste information, which enables more efficient interaction with Gemini. On the other hand, the portability of the mobile app makes it possible to use the model “on the go,” simplifying quick queries during travel.

Nevertheless, Google regularly releases updates, and Gemini’s functionality is constantly evolving. Therefore, the differences between the web version and the mobile app change over time.

Gemini Assistant

On many smartphones running the Android operating system, the Gemini model is gradually replacing the classic Google Assistant.

That is, when you long-press the central button or say the phrase “Hey Google,” Gemini launches. It accepts the same voice commands but generates more accurate responses with expanded explanations and consolidated information from different apps. This may also include functions for managing messages, photos, alarms, timers, smart home devices, and much more.

Some smartphone manufacturers specifically add a quick-access Gemini button directly to the lock screen, allowing you to instantly continue a conversation or ask a question without unlocking the phone.

Thus, Gemini is gradually bringing together multiple functions, transforming into a unified smart control center for the phone. And most likely, this trend will only continue.

Chrome Browser

In new versions of Google’s Chrome browser, the Gemini neural network is built in by default and is available via an icon in the toolbar or by pressing a hotkey.

This way, on any page, you can run queries to analyze text, create a summary, or provide brief explanations of the content of the open site.

And let’s not forget third-party extensions that allow Gemini to be integrated into the browser, expanding its basic functionality.

Google Ecosystem Services

On paid plans, Gemini is available in many Google Workflow services. It adds interactivity to working with documents and content:

• Gmail. Helps draft and edit emails based on bullet points or existing text.
• Docs. Generates article drafts and edits text and sentence style.
• Slides. Instantly creates multiple versions of illustrations and graphics based on a description of the required visuals.
• Drive. Summarizes document contents, extracts key metrics, and generates information cards directly in the service interface.

This is only a small list of apps in the Google ecosystem where you can use Gemini. The main point of integrating the model into services is to automate routine tasks and reduce the burden on the user.

Plugins and Extensions for Third-Party Applications

For third-party applications, separate plugins are available for integration with Gemini. The most common are extensions for IDE editors, messengers, and CRM systems.

For example, there is the official Gemini Code Assist extension, which embeds Gemini into integrated development environments such as Visual Studio Code and JetBrains IDEs. It provides autocomplete, code generation and transformation, as well as a built-in chat and links to source documentation.

There are also unofficial plugins for CRM systems like Salesforce and HubSpot, as well as for messengers like Slack and Teams. In these, Gemini helps generate ad copy and support responses, as well as automates workflows through the API.

Versions and Pricing Plans for Gemini

First, Google offers both free and paid plans for personal use:

Free. A basic plan with limited functionality. Suitable for most standard tasks. Free of charge.

• Access to basic models, Gemini Flash and Gemini Pro. The first is optimized for fast and simple tasks, the second offers more advanced features but with limitations.
• Limited context window size up to 32,000 tokens (equivalent to about 50 pages of text).
• No integration with Google Workspace apps (Gmail, Docs, and others).
• No video generation functions.
• Data may be used to improve models (this can be disabled in settings, but it is enabled by default).
• Limited usage quotas for more advanced models and functions.

Advanced. An enhanced plan with extended functionality. Suitable for complex tasks requiring deep data analysis. Pricing starts at $20/month.

• Access to advanced and experimental models without restrictions.
• Increased context window size up to 1 million tokens (equivalent to about 1,500 pages of text or 30,000 lines of code).
• Deep integration with Google Workspace apps.
• Image and video generation functions.
• Data is not used to improve models.
• Expanded voice interaction capabilities via Gemini Live, including the ability to show objects through the camera.
• Priority access to future AI features and updates.

Second, there are extended plans for commercial (business) and non-commercial (educational) organizations, offering additional collaboration and management features:

• Business. Provides extended functionality of the Advanced plan with additional tools for team use. Designed for small and medium businesses. Pricing starts at $24/month.
• Enterprise. Provides extended functionality of the Business plan with additional tools for AI meeting summaries, improved audio and video quality, data privacy, and security protection. It also has higher limits and increased priority access. Designed for large international companies with high security and scalability requirements. Pricing starts at $36/month.
• Education. Provides full access to Gemini’s generative capabilities for educational institutions, including many additional features tailored to the learning environment. Custom pricing.

Gemini API for Developers

Specifically for developers engaged in machine learning and building services based on large language models, Google provides a full API for interacting with Gemini without a graphical user interface.

Moreover, Google has separate cloud platforms for more efficient development and testing of applications built with the Gemini API:

• Google AI Studio. A lightweight and accessible platform designed for developers, students, and researchers who want to quickly experiment with generative models, particularly the Gemini family from Google. The tool is focused on working with large language models (LLMs): it allows you to quickly create and test prompts, adjust model parameters, and get generated content. The platform offers an intuitive interface without requiring deep immersion into machine learning infrastructure. Simply put, it’s a full-fledged sandbox for a quick start in the AI industry.

• Vertex AI. A comprehensive artificial intelligence and machine learning platform in Google Cloud, designed to simplify the development, deployment, and scaling of models. It combines various tools and services into a unified, consistent workflow. Essentially, it is a unified set of APIs for the entire AI lifecycle, from data preparation to training, evaluation, deployment, and monitoring of models. In short, it is a complete specialized ecosystem.

• Gemini Gems. A set of features in Google Gemini designed to automate repetitive tasks and fine-tune model behavior. It allows you to create mini-models tailored for specific, narrow tasks: creating recipes, writing code, generating ideas, translating text, assisting with learning, and much more. In addition to manual configuration, there are many ready-made templates.

Naturally, Google provides the API as a separate channel for interacting with Gemini. With its help, developers can integrate text generation, code writing, image processing, audio, and video capabilities directly into their applications.

Access to the API is possible through the Google Cloud computing platform. Working with Gemini without a graphical user interface is a separate topic beyond the scope of this article. You can find more detailed information about the Gemini API in the official Google Cloud documentation.

Nevertheless, it can be said with certainty that working with the Gemini API is no different from working with the API of any other service. For example, here is a simple Python code that performs several text generation requests:

from google import genai

# client initialization
client = genai.Client(api_key="AUTHORIZATION_TOKEN")

# one-time text generation
response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents="Explain in simple words how generative AI works",
)

print(response.text)

# step-by-step text generation
for chunk in client.models.stream_generate_content(
    model="gemini-2.0-pro",
    contents="Write a poem about spring",
):
    print(chunk.text, end="", flush=True)

At the same time, Google provides numerous reference materials to help you master cloud-based AI generation:

• Documentation. Official reference for all possible capabilities and functions of the Gemini API.
• GitHub Examples. Numerous examples of using the Gemini API in Go, JavaScript, Python, and Java.
• GitHub Cookbook. Practical materials explaining how to use the Gemini API with ready-made script examples.

Thus, Gemini offers developers special conditions and tools for integrating the model into the logic of other applications. This is not surprising, since Google has one of the largest cloud infrastructures in the world.

Conclusion

The Gemini model stands out favorably from many other LLM neural networks, supporting working with multimodal data: text, code, images, and video.

Google, with its rich ecosystem, seeks to integrate Gemini into all its services, adding flexibility to the classic user experience.