-: Hey, and in this video,
you're gonna learn about the least to most technique
and how you can utilize that inside of ChatGPT.
Well, what is least to most?
Basically, you don't always have to have all of the context
inside of a large language model to get started with a task.
Maybe just start with a couple of bits of context
and then iterate on the output
that you get back from ChatGPT or Claude or Gemini.
And then once you've reached a final output
that you're happy with, then you can consider the task done.
Let's have a look at an example of this.
For example, if we were creating an article outline about
what type of food supplements I should take, we could say,
I want to create an article outline
about different types of food supplements
that I want to take.
And there's not that much context within this prompt,
and because of that, you are going to get back
quite a vague response.
We want to give it more context in the introduction
so we can select this section
and then we can say, improve the introduction
and make sure to include habits, frequency, et cetera.
Expand this section, but keep the rest the same.
And so the idea is you're basically taking
a very vague response
when you didn't have a lot of original context,
and as you see ChatGPT generate a response,
you are iterating through the output
and you'll basically keep doing this
until you figure out exactly what kind of format
you want for the output.
The benefit of doing this means that you don't have
to have a very large prompt at the start.
You can work iteratively with ChatGPT,
and you can get a refined output
by selectively saying this needs to change
or that needs to change.
In the next video, we'll have a look at some best practices
for how you can make sure
you get better outputs from ChatGPT.