Okay, so let's talk about how language myth solves the main challenge that you will face during the
prototyping phase.
So let's go to the platform and we can see how all this applies in the platform.
So the first thing is easy to have language myth enabled since day one is just doing what we, uh,
we saw in the in the previous lesson.
So you just include the language Smith variables in the dot m file of your Lang Lang chain project,
and that's it.
Lang Lang Smith is going to be working as you advance in your project.
Okay.
So this is very interesting in one side.
But you also have to, uh, be aware of the cost.
Okay.
So as you know, your free account with Lang Smith has, I think, 8000, uh, traces included in the
first month.
So, I mean, have that in mind.
If your project is a small project, probably you are going to be totally fine.
But be careful, because if your project is a large project and you have a lot of traces in the first
month, it can be costly for you.
So pay attention to that.
But it is going to be very interesting in principle to have Lang Smith enabled since day one.
Second, how to use the Lang Smith Playground to iterate and experiment.
How to experiment with prompts in the Lang Smith Hub prompt playground environment.
This is a very long name, so, uh, if you remember, we have what they call the hub, which is where
you can find a prompt examples that you can you can reuse.
And once you select one of these prompt examples, you can experiment with it.
Going to the playground okay.
So you go to the try it button.
You come to the playground.
So they have uh and the playground are not exactly the same.
But you can say they go together.
Right.
So one way to use the the playground is and this way we show you it's like, okay, you go to the hub,
you select one prompt and then you click on the try it button.
And you go to the playground.
And as you can see in this screen, in this playground, you can play with different things.
You can change, you know, the prompt.
Uh, so the system, uh, template, the human, uh, template, you can uh, play around with uh,
inputs etc. and you can also play around with models.
Okay.
So interestingly they are providing two free models, A and if you want to use any of the paying models
you will have to include your API key.
We are going to see how to do that later.
And then you also can play around with the features of the model.
Okay, so this is one way of using the playground.
So we will we will we will get to the to the second way of using it.
So you can use it just by going to the to hub.
In the Language Myth hub you will find examples of prompts.
You go to the language Myth Hub.
You will see example.
You will find examples of prompts that other developers are using for many different cases purpose,
model, etc..
Clicking on a prompt example will open the prompt playground environment.
Not exactly because it's you need a second click.
You need to click in the try it, but more or less.
One interesting feature here is prompt versioning.
With this you can you can see how the initial prompt has evolved with the contributions of different
people.
This can be an interesting way of collaborating between developers and subject matter experts, product
managers, managers, marketing people, etc. so this is interesting, uh, because sometimes the LM
app developer doesn't have the proper knowledge in order to write a correct prompt, and it is going
to be the feedback from the product manager or marketing people, or subject matter expert who help
the LM app developer to use the correct prompt.
Do you remember the example we use a few lessons ago when when we were talking about, for example,
a legal application, right?
So we are going to develop an application that is going to help lawyers doing whatever.
So in this particular field is going to be very interesting to have one expert, one subject matter
expert included in your team, because he is going to help you not just to design the solution, but
also to interpret properly the feedback that you are getting from your users.
Because since you are not an expert in the field, you can be, uh, wrong in your, uh, conclusions.
Okay, so this is an interesting feature.
The prompt, a prompt versioning.
You can import a prompt from the Lang Smith have into your long chain application without having to
copy the entire prompt.
And here you have a link, uh, to to see detailed instructions on how to do this.
Okay.
This this is interesting, but the interesting thing is that you can use it from any trace.
So as I was telling you, you can go to one of your projects, you can open any of your traces.
And from the trace you can go to the playground.
And in the playground you can start playing around.
So you can open any trace in this prompt playground environment and change the prompt, the LM model
or the LM model features like temperature, etc..
There are two LM models you can use here for free.
Okay.
And yeah, fireworks and Google Pan.
Okay.
To use ChatGPT, you will need to enter your OpenAI API key.
When you are in the playground, there is a button in the right top corner called secrets and API keys.
To do that you see this button.
This is where you will enter your OpenAI key.
Okay.
Next solution.
How to use Lang Smith comparison view to compare the performance of alternative approaches.
So if you go to the data set dashboard go to one data set.
Select several tests.
In this case we only have one, but you would select several tests that you perform using that data
set and click on the compare button.
You see here the compare button.
To see the comparison view.
We can use this to compare several tests, compare outputs, compare performance, etc. so the best
way to see this in action is where we look at one professional project.
Okay, so with one professional project you are going to see the full potential of this platform because
you will see all the different examples and it's going to be super interesting for you.
But you you get an initial idea now.
And finally, the four way that Lang Smith is going to help us during the prototyping phase is.
Creating a test data set and include trace include tracing trace examples in it.
So this is this is a very easy.
So in the data set uh dashboard we can create a new data set with this button.
And once we have a new data set we can include any trace.
In the data set just by clicking here.
Okay.
You can click here in any trace.
And the trace will be included in the test data set.
Or when you are reviewing the the particular trace you can also use the button here.
So remember why.
Do we use a test data set?
Remember that in the test data set, we have the correct inputs and outputs that we use in order to
test different prototypes or different versions to check their accuracy, their, uh, latency, their
cost, the number of tokens, etc., etc..
Right.
So here is this is how you can use language myth to create a test data set and include trace examples
in it.
So in the next lesson we are going to see some very interesting advanced tips.
Uh, regarding language myth data sets.
We are going to see this in the next lesson.
It's going to be also very interesting.