- [Instructor] Another place
that you can work with Gemini Pro and Gemini Pro Vision
is in Google Cloud platform.
Now, what I've done is I've set up a project
for demonstration called Gemini Course Langit
on Google Cloud, and I have navigated to Vertex AI.
There are a number of different capabilities
in Vertex AI for Gemini Pro.
But to start, we're going to go to Vertex AI Studio,
which is the location inside of Google Cloud
where you can work with prompt design.
So if you watch the previous set of movies,
this is an alternative interface,
if you have access to Google Cloud,
to the Studio environment.
So if I open this up and I click on overview,
you can see that Vertex AI Studio, as it says here,
lets you quickly test and customize gen AI models
so you can leverage the capabilities in your application.
And you have the documentation
and you have the API reference.
And the sections we have here are multimodal, with Gemini.
We have Language, Vision, and Speech.
So if we want to go ahead and try prompting here.
Notice this is new, as of this recording.
We can click Try It Now.
And we're in an environment where we can test prompts.
So you can see that we have the ability to write a prompt,
and by default, the model is Gemini Pro Vision.
And do you remember the difference between Gemini Pro
and Gemini Pro Vision?
Gemini Pro is for text input only,
Gemini Pro Vision is multimodal.
So it's for text and images.
So if I wanted to just do text, you can still do it here.
So, "List the top 10 tourist
attractions in Minneapolis,"
continuing our Minnesota theme here.
And then we can click Submit.
Notice this little microphone,
so you can actually speak your prompt as well.
And inside of here, here we have our response.
And this is formatted as a list.
So, again, this is very similar in look and feel
to the Studio IDE that we were just working in.
So, basically, it depends on
where you want to do your development
because Vertex AI is for more than just LLM.
Whereas the other tool that I was showing
is designed, really,
for prompt testing.
This has all those features and a whole lot more.
So you can see you have the temperature setting,
you have token limits, you have Advanced.
The top K, top P, that we saw before,
and you have the ability to get code.
Now, this code is going to be a little bit different
because this code is going to open up a Jupyter Notebook
that is part of Vertex AI,
which we'll be doing in subsequent movies,
rather than out on Colab,
which is separate from Vertex AI.
And if you like your prompt, you can save it.
So we'll call this "Minnesota Tourism."
And notice I'm saving it into a region
'cause that's the way Google Cloud works.
And then I have that prompt available.
So, similar but not the same, in terms of prompt testing,
so that you can start your LLM application journey
inside of Vertex AI here
with that same process of prompting.