All right.
Now it's the moment of truth.
We now read the LinkedIn profile in as before, and hopefully this is your LinkedIn profile, not mine.
And then we bring in the summary text.
You should change this name to be your name.
We run that and we now have our system prompt and user prompt for the LM.
And the system prompt is going to be the same as last time you're acting as me or you.
You're answering questions on their website, particularly related to their career, etc., etc. but
there's a twist at the end here.
It says if you don't know the answer to any question, use your tool to record the question you couldn't
answer.
And if the user is engaging in discussion, try to steer them towards getting in touch via email.
Record it using your tool.
Now, in theory, this isn't needed because the JSON that we've written to describe the tool already
gives this kind of context and describes the tool and when it should be used.
But it never hurts to be repetitive in prompting, as I'm sure you know, repetition always works well.
Explain things several times over, because you will increase the probability that your model performs
the way you want it to.
You're biasing the model to be outputting tokens consistent with your objective, and it is important
to always keep that in the back of your mind.
We often like to talk about.
You know, the model calls this tool and we make it sound like it almost that it's almost got something
that has true autonomy.
But all that's actually going on here.
And always remind yourself of this from time to time, an LM is just something that's generating the
most likely next tokens to follow a sequence of tokens.
It's got an input and it's generating like predictive text.
What is the most likely text to follow this?
And if we've inserted into this prompt stuff about different tools that it could call and stuff that
directs it to use them, then that just means that the most likely next tokens will be consistent with
that information that we passed in.
And that's all that's going on.
It's about statistical most likely tokens.
And that really I mean, it hurts my mind to try and think that one through, because it seems so unlikely
that predicting next tokens could be so powerful as to do things like calling tools.
But that really is what's happening here.
All right, that was a sidebar, but it's an important sidebar.
You should always try and keep that in mind.
And I have a bunch of YouTube videos about that stuff.
If you want to learn more about how Next token prediction is able to achieve these things.
Okay, so with all of this we get to our chat function again.
And this is where it really comes down to action.
And we'll cover it in the next video.