Artificial intelligence (AI) is exploring its capabilities and developing some exciting innovations. Google Gemini is one of the most notable examples we’ve seen.
Google has recently released its own AI-powered Chatbot called Gemini (formerly known as Google Bard), which makes searches more immersive and smoother for consumers. It obtained tremendous popularity in short-time due of its remarkable functions and how it allows individuals to make their day-to-day operations easier.
If you’re tech-savvy and want to know how you can use Gemini in your day-to-day life then this article is for you.
In this article, we will go over everything there is to know about Gemini, including how to use it, some unique features, limitations, and benefits. Let’s go explore!
Gemini AI is Google’s forthcoming AI model, created by specialists from their combined AI teams of DeepMind and Google Brain. This technology is set to rival OpenAI’s GPT-4 and is anticipated to be a considerable leap forward in the domain of natural language processing.
Gemini’s multimodal capabilities, which allows it to handle various forms of data concurrently, is one of its most notable qualities. This approach is specifically built to handle both text and images, allowing for novel functionalities such as textual evaluations of visual graphs. Furthermore, Google intends to improve Gemini’s code-generation capabilities, positioning it as a competitor to Microsoft’s GitHub Copilot, which uses OpenAI technology.
In this blog article, we will look at how Gemini AI may be used to generate code, summaries, and visual graphs for a variety of reasons. We’ll also provide you some examples and pointers on how to make the most of this amazing tool.
Generating code with Gemini AI
If you are a developer or a coder who wants to save time and effort in writing code, you might be interested in using Gemini AI to generate code snippets based on natural language descriptions or examples. For instance, you can ask Gemini AI to write a function that calculates the factorial of a number in Python, or a class that represents a person in Java.
To use Gemini AI for code generation, you need to access the Google PaLM API with LLama Index4, which is a platform that allows you to build your own AI apps with no coding required. You can also try out our default PaLM API integration5, which provides full guides on how to use the underlying SDK + MakerSuite.
Once you have access to the API, you can use the following steps to generate code with Gemini AI:
Define your task: Write down what kind of code you want Gemini AI to generate. Be as specific as possible and include any relevant details such as input/output formats, parameters, logic flow, etc.
Encode your task: Use the encode method of the PaLM API client library or MakerSuite SDK to convert your task into a common language that Gemini AI can understand. You can also use natural language queries such as “write me a function that does X” or “create me a class that has Y methods”.
Generate code: Use the decode method of the PaLM API client library or MakerSuite SDK to convert the encoded input into an output format that contains the generated code. You can also use natural language feedback such as “make it more concise” or “add comments” to improve the quality of the generated code.
Test and deploy: Use any IDE or editor of your choice to run and debug the generated code. You can also deploy your app using any hosting service or platform that supports Python or Java.
Here is an example of generating Python code with Gemini AI based on this task:
Write me a function that takes two numbers as input and returns their sum.
The encoded input for this task could be:
Sum two numbers
The decoded output for this input could be:
Python
AI-generated code. Review and use carefully.
def sum_two_numbers(a,b):
return a + b
You can try out different tasks and see how Gemini AI generates different kinds of code for various languages such as Python, Java, C#, JavaScript, etc.
Generating summaries with Gemini AI
If you are looking for ways to summarize long texts such as articles, reports, books, etc., you might want to use Gemini AI to generate concise summaries based on natural language queries or examples. For example, you can ask Gemini AI to summarize an article about artificial intelligence in one sentence.
To use Gemini AI for text summarization, you need to access the Google PaLM API with LLama Index4, which is a platform that allows you to build your own AI apps with no coding required. You can also try out our default PaLM API integration5, which provides full guides on how to use the underlying SDK + MakerSuite.
Once you have access to the API, you can use the following steps to generate summaries with Gemini AI:
Define your assignment: Make a list of the kind of summaries you want Gemini AI to generate. Be as precise as possible, including any pertinent data such as length, tone, focus, and so on.
Codify your task: To turn your task into a common language that Gemini AI can comprehend, use the encode method of the PaLM API client library or MakerSuite SDK. Natural language questions such as “Summarize this article in one sentence” or “Give me an overview of this book” can also be used.
Create a summary: To transform the encoded input into an output format, use the decode method of the PaLM API client library or MakerSuite SDK.
Google Gemini is completely free of cost. Simply sign up and you will get access to Google Gemini.
For a more in-depth look, check our Guide to Conversational AI.
Gemini is one of the most popular AI-powered chatbots, assisting billions of people with their daily job activities. Its popularity will grow as additional features and improved outcomes become available.
We hope this guide has helped you learn everything you need to know about Gemini AI, how to utilize it, and how to maximize its output. Now it’s your chance to log in to Gemini and use it to boost your productivity.