Instructor: Large language models
are trained on enormous amounts of data.
It is believed that GPT-3 was trained
of over a billion words.
So just to give you a comparison.
If you were to take a stack of a billion one dollar bills,
this dollar stack would be over 67 miles up high.
Now,
this amount of data is directly translated
to the knowledge of the model.
So it is totally capable of answering questions
and performing instructions
without having some input data provided to it.
So a zero-shot prompt is a type of prompt
in which the model generates an output
for that task it has not been explicitly trained on.
So this means that the model is asked to perform
a task without any specific training data, for example,
for that specific task.
Instead the model uses its preexisting knowledge
to perform the task based on the information provided
in the prompt.
So for example,
a language model that has not been trained on English text
can still generate accurate outputs for French text
despite it not being specifically trained on French.
Now,
let's take a look at an example
of a prompt that is a zero-shot prompt.
So the prompt is,
"Create a list of the 10 must-visit cities
"in the world in no particularly order."
So you can see that we didn't supply it with any examples
or any input data to indicate the answer.
And it listed us a beautiful, coherent answer.
So this is the zero-shot prompt.
Now,
as you go deeper and deeper into prompt engineering,
you'll notice that the zero shot prompt is actually
the most popular kind of prompt that people are using
when they're getting into AI.
Because at the beginning,
you're starting to learning how to interact with the model.
And it's super intuitive to simply ask it questions
without providing it any examples
or any way of thinking.
So zero shot prompting do come with a bunch of limitations.
For example, the accuracy.
So what we're getting back from the model
may not be exactly what we're looking for
because we didn't supply it with any data or any guidance.
So the scope might be limited,
and we definitely have less control
because it's only one prompt
that relies on the model's preexisting knowledge,
and it cannot be fine-tuned to any specific use case.