-: Hey, welcome back.
And in this section we'll be exploring something
called Asking the Model For More Context.
We'll see two different types of scenarios
and how you can apply this.
Let's take something like a very broad prompt,
which is gonna give us not a very specific output,
and see how we could ask the model for more context
about the output that it generates.
In this example, we have a prompt that says,
"Goals: I'm looking to improve my fitness,
health, and wealth.
Provide a very detailed, numerical,
hierarchical outline for the above goals."
You'll see that we get some information
about fitness, health, and wealth.
But we have parts of this are very vague,
we don't really understand them.
We could then ask ChatGPT
to drill down into these by saying,
"Could you please provide more context
about ways that I can do this?"
And so what we're doing is we're taking a very broad goal,
and we're asking the model to give us more context
about what it's decided to do.
As well as that, we could also ask it for more context
about why it decided to generate an output.
So, here we've got, kind of,
a bit more deeper level information
about improving our diet and nutrition.
That's great.
And then as well as that,
let's pick something that's been quite specific.
So maybe "Why should we," Why should we,"
and then, "reduce sugar intake?"
So we can kind of change this into a question
which allows us to really understand,
not just from a broad perspective why we should do things,
but getting some type of evidence.
And so this is quite a useful technique
when you're learning about different types of things.
Maybe there's something you don't understand.
You can even ask it, and ask the model for more context.
"Can you explain this more simply?"
So that can be an example of asking for more context,
is asking for the context in a different way
so that you can understand it better.
So there's a couple of different use cases here.
Let's have another look at a different use case,
which is kind of similar, but slightly different.
So, what we've got in this initial prompt is,
"I'm looking to ask for a promotion at my job,
use the following information to generate
an effective and engaging letter to my boss."
So ChatGPT is now confident
about what the task is meant to be doing,
and we're also confident about the information
that we've given it.
But what happens if we didn't give it all the information
where it could generate a really good answer?
So one way to deal with this is to give ChatGPT
or GPT-3 an out clause,
by saying, "Before writing the letter,
if you don't have all the information
to do the task, avoid,"
and then whatever the task you said it was,
so in this case, "generating an effective
and engaging letter to my boss
and ask for more information."
ChatGPT has decided at this point
that it doesn't have enough information
to make a good output for us.
And so we are kind of using it as an agent.
And then if you'll just give me a second,
I can paste in,
this is provided with all the extra information,
now it's able to then provide a better cover letter
or a better written letter.
So rather than just telling ChatGPT or GPT-3
to produce an output, we can say,
"Hey, if you don't think that you could produce
a reliable output, please ask for more context."
And in this case, the model is asking for more context
and we're not necessarily asking for more context.
And so this can help improve the liabilities of your output
because now ChatGPT has all of the relevant information
inside of it to make a better decision.