Hunting is one of those common techniques.
This is it's really easy to do.
What it is, is just asking the lion to play the role of a specific celebrity or someone that made the
that is a domain expert, but you could also just ask it to play a specific job role.
So you as a math teacher, answer this question.
There is some evidence that it actually does improve the intelligence of the model slightly.
Say you can get better math scores when you tell it to act like a math teacher, but the way that I
tend to use it is really just to get it in the right style.
Because some things are subjective about our responses, and some organizations want something in the
style of Hemingway, whereas another organization might want as something in the style Enid Blyton or
whatever your preferences are.
It is no good answer or right answer, but with role playing, you at least get to a good subjective
preference.
So let me show you an example.
Now we have in here the Citron probe, the role setting and then the normal.
And I use a prompt here.
And just to show you how those things come together for the normal prompt, we're just passing in here.
Brainstorm a list of product names for a shoe that fits any foot size.
That's the task that we wanted to do.
Whereas with the role raw front, we are telling that you are ill and Elon Musk, you're brainstorming
new products and are given some examples.
And this is what I think is necessary to make raw prompting work in Insead, because previously you
should be able to just say you are Elon Musk or whatever, and it would do a decent job, or at least
a better job.
But what I'm finding now is that raw prompting doesn't make that much of a difference, unless you give
it some examples of what your musk would would name this product.
So I made up some fake products here.
So this is a refrigerator that dispenses beer.
And then I just lazily from the call it beer fridge act like space X or not a beer fridge like not a
flamethrower, which showed products that Musk has invented or named.
So that's that's really helpful.
I think it's if you can even make some synthetic examples of what you want and put them in then and
then, and you can get it into the type of character that you want with or that you want this example
here.
So I'm gonna run this and you'll see that if we look at the role play answer, we're getting something
much like your massive universal fit shoe, completely fit all that.
Whereas with the previous example, we were just asking it without all the all of these things, I were
just getting adaptive fit, stride, universal fit, flexi fit.
So I'm not getting Musk like names.
So that's raw quantity in a nutshell.
But that's not where it ends, right?
If we need some evaluation metric.
And typically for this type of evaluation metric you need an LM to check essentially whether there as
a judge whether you are following IL must die or should we have another prompt here which says is a
language model with knowledge of Elon Musk's style and naming convention, to evaluate the following
product names and determine if they sound like they're created by Elon Musk.
So we have the product name, and then we need to provide the evaluation as a percentage.
And then we have some template here that we've said like product X or the product for free or not product.
So I find that this really helped if you're giving it some examples, just like adding examples to previous
prompts really helped as well.
And what we're responding with here is instead of the product description and product names, were responding
with an explanation.
And then the Musk likelihood.
Now the reason I do it this way round, by the way, is that if you ask it to provide an explanation
first, or some analysis or thinking first, it does a better job of getting an accurate percentage
or a reliable percentage that you know will come back again and again if you run it multiple times.
And so this like a small chain of thought implementation.
All right.
So we're going to run that.
I'm just going to see what we're doing is running the role play names with the SAT names and just seeing
which ones are more muscular.
All play names do really well.
We have 90% 100% likely to be Elon Musk.
We have to have the explanation.
So if we don't agree how we can just check this and see.
I think that that works really well.
And then if we look at the standard name, we've got a 60%, 50%, 70%.
So we can really tell the difference.
Like these names still sound like Musk according to it because it's playful.
Suggest innovation lacks a boldness, a whimsical nature often found in Musk's branding.
Yeah.
So yeah, we could optimize maybe the the percentages here by changing this prompt.
And obviously this is a prompt as well.
So we need to optimize this prompt too.
But it's usually much easier to optimize an evaluation prompt because you can rewrite the percentages
yourself and say, this should have been 20%, and then you could test again.
This evaluator and then get to a prompt that gets it right.
And that's accurately repeating the 20% rule.
So now you have this.
You could a B test your original prompt.
Now you can evaluate your last name and then go back and just check whether it's actually following
that rule or not.