Expanding is the task of taking a short piece of text,

such as a set of instructions or a list of topics,

and having the large language model generate a

longer piece of text, such as an email or

an essay about some topic. There are some great uses of this,

such as if you use a large language model as a brainstorming partner.

But I just also want to acknowledge that

there are some problematic use cases of this,

such as if someone were to use it, they generate a large amount of spam.

So when you use these capabilities of a large language model,

please use it only in a responsible way and in

a way that helps people.

In this video we’ll go through an example of how you can

use a language model to generate a personalized

email based on some information. The

email is kind of self-proclaimed to be from an AI

bot which as Andrew mentioned is very important.

We’re also going to use another one of the models input

parameters called temperature and this kind of allows

you to vary the kind of degree of exploration and variety in

the kind of models responses. So let’s get into it.

So before we get started we’re going to kind of

do the usual setup. So set up the OpenAI Python package and then also define

our helper function getCompletion

and now we’re going to write a custom email response to

a customer review and so given a customer review and the sentiment

we’re going to generate a custom response. Now we’re

going to use the language model to generate a custom

email to a customer based on a customer

review and the sentiment of the review. So we’ve already

extracted the sentiment

using the kind of prompts that we saw

in the inferring video

and then this is the customer review for a blender

and now we’re going to customize the reply

based on the sentiment.

And so here the instruction is you are a customer service AI

assistant your task is to send an email reply to about your

customer given the customer email delimited

by three backticks generate a reply to thank the customer for their

review. If the sentiment is positive or neutral thank

them for their review. If the sentiment is

negative apologize and suggest that they can reach

out to customer service. Make sure to use

specific details from the review write in a

concise and professional tone and sign the email

as AI customer agent. And when you’re using a language model to

generate text that you’re going to show to a user it’s very important

to have this kind of transparency and let

the user know that the text they’re seeing was generated

by AI.

And then we’ll just input the customer review

and the review sentiment. And also note that this part isn’t necessarily

important because we could actually use this prompt to

also extract the review sentiment and then in a follow-up step write

the email. But just for the sake of the example, well, we’ve already

extracted the sentiment from the review. And so, here we have a

response to the customer. It kind of addresses

details that the customer mentioned in their review.

And kind of as we instructed, suggests that they reach

out to customer service because this is just

an AI customer service agent.

Next, we’re going to use a parameter of the language

model called temperature that will allow us to

change the kind of variety of the model’s responses. So you can kind of

think of temperature as the degree of exploration

or kind of randomness of the model. And so, for

this particular phrase, my favourite food is the

kind of most likely next word that the

model predicts is pizza and the kind of next to most likely

it suggests are sushi and tacos. And so, at

a temperature of zero, the model will always choose the

most likely next word, which in this case is pizza, and

at a higher temperature,

it will kind of also choose one of the less likely words

and at an even higher temperature, it might even choose tacos,

which only kind of has a five percent

chance of being chosen.

And you can imagine that kind of,

as the model continues this final response, so my favourite food is

pizza and it kind of continues to generate more words,

this response will kind of diverge from the response,

the first response, which is my favourite food is tacos.

And so, as the kind of model continues,

these two responses will become more and more different.

In general, when building applications

where you want a kind of predictable response,

I would recommend using temperature zero.

Throughout all of these videos, we’ve been using temperature zero and

I think that if you’re trying to build a system that is

reliable and predictable, you should go with this. If you’re trying to

kind of use the model in a more creative way where you

might kind of want

a kind of wider variety of different outputs,

you might want to use a higher temperature. So,

now let’s take this same prompt that we just used and let’s try

generating an email, but let’s use a higher temperature. So, in our getCompletion

function that we’ve been using throughout the videos, we have kind of

specified a model and then also a temperature,

but we’ve kind of set them to default. So, now let’s try varying the

temperature.

So, we’ll use the prompt and then

let’s try temperature 0.7.

And so, with temperature 0, every time you execute the same prompt,

you should expect the same completion. Whereas with temperature 0.7, you’ll get

a different output every time. So, here we have

our email, and as you can see, it’s different to the email that we

kind of received previously. And let’s just execute it again, to show

that we’ll get a different email again.

And here we have another different email. And so, I recommend that

you kind of play around with temperature yourself.

Maybe you could pause the video now and

try this prompt with a variety of different temperatures,

just to see how the outputs vary.

So, to summarise, at higher temperatures,

the outputs from the model are kind of more random.

You can almost think of it as that at higher temperatures,

the assistant is more distractible, but maybe more creative.

In the next video, we’re going to talk more about the

Chat Completions Endpoint format, and

how you can create a custom chatbot using this format.