Okay, so this is probably the production level.
Uh, LM application, the professional, uh, LM application that is making a more sophisticated use
of, uh, the Smith platform.
This is the chat dot Lang Chain.com project.
This is a support chat created by the Lang Chain team.
And here in this notebook, you will see the second notebook.
You will see in the in the in this blog you have a link to a video, uh, where uh, they explain in
detail how this professional project is using Lang Smith.
I will highlight a few screens of that video.
But the interesting thing about this project is that we have the Lang Chain team, uh, explaining us
how they use Lang Smith.
And you will see they serve very interesting information, but they also share the code of this project.
So here in the notebook you have also a link to the GitHub repository, where there they share the code
of this project.
And within the code of this project you can see also sophisticated code related to Lang Smith.
So some of the screens I have selected for you.
So this is a video you can find in the YouTube channel of Lang Chain.
And in February 2024 a the uh.
The person responsible from the team in in in long chain for the the project long Smith, which is one
of the co-founders of long chain is explaining us in this video is like a one hour video.
How do they use Lang Smith in the in the project?
And this is a very interesting project because as you can see here in this screen, this is a project
that is using like, you know, hundreds of thousands of tokens.
They are spending a significant amount of money, uh, in, in, in, in open AI, you know, to have
this project running, they are using different models, but in open AI they are they are making an
interesting investment.
And this is probably the project that is making the best use of Lang Smith, because this is a project
developed by the the, the Lang Smith developers.
Right.
So so it's very interesting to see, you know, under the hood what they are uh, doing with Lang Smith.
So this is one of the screens that I have selected for you.
I have selected just a few screens just to tell you what you are going to find when you see the video.
I recommend you to see this video.
To study this video in detail, you will see things like.
If you remember, we talk about the metadata and the different metadata possibilities you can use in
order to monitor and and make a B, testing, etc..
So this is another screen that is going to be very interesting for you.
Look at the metadata.
You can you can see in this project also here you have uh in the monitor dashboard how they are comparing
different versions of the of the project.
So very interesting for you to take a look at this and also take a look at the right side bar where
they are sharing some of the some of the different tags and filtering options they are working with.
You have here the different test.
Uh, do you remember that in our basic example we only use one test in the data set.
So here you have different tests and the comparisons they are doing.
And finally here you have also a more detailed comparison between different versions.
Okay.
So I just wanted to tell you that we are very lucky to have uh, this video from the Long Chain team.
Uh, I don't know how to call it the long chain team or the long Smith team.
You know, they are the same company, but, uh, anyway, Long Chain or Lang Smith has shared this
video with us.
They also have shared the code of the application with us in GitHub.
And I think this is an invaluable resource for us because the creators of Lang Smith are showing us
how they are using Lang Smith for their own projects.
So very, very interesting project.
I, I really recommend you to go here, uh, and, and check this video, study this video in detail
because they are sharing a very, uh, a number of very interesting, uh, uh, subjects with us and
also recommendations and uh, advices.
So very, very interesting.
In my opinion, this is right now the best source of information.
You want to see how a professional project is using.
Uh, Lang Smith today.