Exploring Vertex AI Studio Playground (Gemini chat, code, multimodal)

Introduction to Vertex AI Studio Playground

Vertex AI Studio is where developers experiment with Gemini models before building real AI applications.

In this video, you will learn how to:

• open Vertex AI Studio
• understand the Playground interface
• test Gemini chat prompts
• generate and explain code
• explore multimodal capabilities like image input

By the end of this lesson, you will know how developers test ideas in Vertex AI before integrating AI into applications.

Open Google Cloud

First, open your browser and go to the Google Cloud Console.

https://console.cloud.google.com/

Log in using your Google account.

If this is your first time using Google Cloud, you may need to create a project.

Google provides a simple guide for beginners here:

https://cloud.google.com/docs/get-started

Once you are inside the console, look at the search bar at the top.

Type Vertex AI.

Click Vertex AI from the results.

Now look at the menu on the left side.

Click Vertex AI Studio.

If you want to open it directly in the future, you can bookmark this page:

https://console.cloud.google.com/vertex-ai/generative

You are now inside the workspace where you can test Gemini models.

What Vertex AI Studio Is

Vertex AI Studio is a testing environment for generative AI models.

Developers use it to:

• experiment with prompts
• explore how the model responds
• adjust model settings
• validate ideas before writing application code

Instead of building an entire application first, developers test their ideas here.

Once they find prompts and workflows that work well, they integrate them into real applications using APIs.

You can learn more about Vertex AI generative tools here:

https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview

Understanding the Playground Interface

Let’s look at the main parts of the Playground interface.

You will usually see four key areas.

First is the model selector.
This allows you to choose the Gemini model you want to use.

Second is the prompt input area.
This is where you type your instructions.

Third is the model settings panel.
Here you can adjust parameters like creativity and output behavior.

Fourth is the output section.
This is where the model’s response appears after you run a prompt.

The workflow is simple.

Choose a model, write a prompt, run it, and review the response.

This is how developers test ideas before building applications.

Testing Gemini Chat Mode

Let’s start by testing chat mode.

Chat mode is useful for tasks like:

• answering questions
• summarizing information
• drafting text
• brainstorming ideas

In the prompt area, type the following instruction.

Explain cloud computing in simple terms for a college student.

Click Run.

The model generates a response.

Now let’s improve the prompt.

Developers rarely stop at the first prompt. They improve it step by step.

Try this prompt instead.

Explain cloud computing in simple terms for a college student. Use short paragraphs and one real world example.

Run the prompt again.

Notice how the response becomes clearer and more structured.

This process is called prompt iteration.

Developers refine prompts in the Playground until they get reliable output.

You can learn more about prompt engineering here:

https://cloud.google.com/vertex-ai/docs/generative-ai/prompt-engineering

Testing Code Generation

Gemini can also assist with programming tasks.

It can help with:

• generating code
• explaining code
• debugging code
• converting code between languages

Let’s try a simple example.

Type the following prompt.

Write a Python function that returns the average of a list of numbers.

Run the prompt.

Gemini generates the Python code.

Now let’s try a different type of coding request.

Explain this Python function line by line:

def average(nums):
return sum(nums) / len(nums)

Run the prompt again.

Now Gemini explains the code step by step.

This is useful for developers building coding assistants or for learners who want help understanding code.

You can explore the Gemini API documentation here:

https://ai.google.dev/docs

Testing Multimodal Input

Modern AI models can understand more than text.

They can also work with images and other types of input.

This capability is called multimodal AI.

Depending on the interface and model, the Playground may allow you to upload an image.

Upload a simple image or screenshot.

Now enter this prompt.

Describe what is happening in this image in simple language.

Run the prompt.

The model now combines the image and the text instruction to produce a response.

Multimodal AI is useful for many real-world applications, including:

• analyzing charts and diagrams
• describing images
• reading screenshots
• document analysis

You can learn more about Gemini’s multimodal capabilities here:

https://ai.google.dev/gemini-api/docs/multimodal

Understanding Model Settings

You may notice some model settings on the side panel.

One important setting is temperature.

Temperature controls how creative or varied the response is.

Lower temperature usually produces:

• more focused answers
• more consistent responses

Higher temperature produces:

• more creative responses
• more variation in wording

For enterprise tools like customer support systems or document analysis, developers often use lower temperature.

For creative writing or brainstorming tools, higher temperature may be useful.

More details about model parameters can be found here:

https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference

How Developers Use the Playground

Here is the typical workflow developers follow when using Vertex AI Studio.

First, they define a use case.

Second, they write a prompt.

Third, they run the prompt and review the response.

Fourth, they refine the prompt to improve the output.

They repeat this process until the response is reliable and useful.

Once the prompt works well, they connect the model to their application using APIs.

This approach saves time and helps developers build better AI applications.

Summary – Key Takeaways

You learned how to:

• open Vertex AI Studio in Google Cloud
• understand the Playground interface
• test Gemini chat prompts
• generate and explain code
• experiment with multimodal inputs
• adjust model settings

Vertex AI Studio is the place where developers experiment and test ideas before building AI-powered applications.

In the next lesson, we will move from testing prompts to making our first Gemini API call using Python and Google Colab.