DeepLearningAI - ChatGPT Prompt Engineering for Developers - Summarizing

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There’s so much text in today’s world, pretty much none of us have

enough time to read all the things we wish we had time to. So one

of the most exciting applications I’ve seen of

large language models is to use it to

summarise text. And this is something that I’m seeing multiple teams

build into multiple software applications. You can do this

in the Chat GPT Web Interface. I do this all

the time to summarise articles so I can just kind of read the content of many

more articles than I previously could. And if

you want to do this more programmatically, you’ll see how to

in this lesson. So with that, let’s dig into the code to

see how you could use this yourself to summarise text.

So let’s start off with the same starter code as you saw

before of importOpenAI, load the API key and here’s that

getCompletion helper function.

I’m going to use as the running example, the

task of summarising this product review. Got

this panda plush toy from a daughter’s birthday

who loves it and takes it everywhere and so on

and so on. If you’re building an e-commerce website

and there’s just a large volume of reviews, having

a tool to summarise the lengthy reviews could

give you a way to very quickly glance

over more reviews to get a better sense of what all your

customers are thinking. So here’s a

prompt for generating a summary. Your task is to generate a

short summary of a product review from e-commerce websites, summarise

the review below and so on in at

most 30 words.

And so this is soft and cute panda plush toy loved by

a daughter but small to the price, arrived early. Not bad, it’s

a pretty good summary. And as you saw in the previous video, you

can also play with things like controlling the character

count or the number of sentences to affect the length of this

summary. Now, sometimes when creating a summary, if

you have a very specific purpose in mind

for the summary, for example, if you want to give feedback

to the shipping department, you can also modify the prompt to

reflect that so that it can generate a summary that is more

applicable to one particular group in

your business. So, for example, if I add to give feedback

to the

shipping department,

let’s say I change this to start to focus on

any aspects that mention.

shipping and delivery of the product. And if I run this, then

again, you get a summary, but instead of starting

off with Soft and Cute Panda Plush Toy,

it now focuses on the fact that it arrived a day earlier

than expected. And then it still has, you know, other details. Or

as another example, if we aren’t trying to give feedback

to the shipping department, but let’s say we want to give feedback

to the pricing department.

So the pricing department is

responsible for determining the price of the product.


I’m going to tell it to focus on

any aspects that are relevant to the price and perceived value.

Then this generates a different summary

that says maybe the price may be too high for its size. Now,

in the summaries that I’ve generated for the

shipping department or the pricing department, it

focuses a bit more on information relevant to

those specific departments. And in fact, feel free to pause

the video now and maybe ask it to generate information for the

product department responsible for the customer

experience of the product.

Or for something else that you think might

be related to an e-commerce site.

But in these summaries, even though it

generated the information relevant to shipping,

it had some other information too, which you could decide may

or may not be hopeful.

So depending on how you want to summarize it,

you can also ask it to extract information

rather than summarize it. So here’s a prompt that says you’re tasked

to extract relevant information to give

feedback to the shipping department. And now it just says

product arrived the day earlier than expected without all

of the other information, which was

also hopeful in the general summary, but less

specific to the shipping department if all it wants to know is

what happened with the shipping.

Lastly, let me just share with you a concrete

example for how to use this in a workflow to help summarize

multiple reviews to make them easier to read.

So, here are a few reviews. This is kind of long, but you know,

here’s the second review for a standing lamp, needle

lamp on the bedroom. Here’s the third review for an

electric toothbrush. My dental hygienist recommended it. Kind of

a long review about an electric toothbrush. This is

a review for a blender when they said, so, so that

17 piece system on seasonal sale and so

on and so on. This is actually a lot of text. If you

want, feel free to pause the video and read through all

this text. But what if you want to know what these reviewers

wrote without having to stop and read all this in detail. So

I’m going to set review 1

to be just the product review that we had up there. And

I’m going to put all of these reviews into a list. And

now if I implement a

for loop over the reviews.

So here’s my prompt and here I’ve asked it to summarize it in

at most 20 words. Then let’s have it

get the response and print it out. And let’s run that.

And it prints out the first review was that Pantatoi

review, summary review of the lamp, summary review of the toothbrush,

and then the blender.

And so if you have

a website where you have hundreds of reviews,

you can imagine how you might use this

to build a dashboard to take huge numbers of reviews,

generate short summaries of them so that

you or someone else can browse the reviews much more quickly.

And then if they wish, maybe click in to

see the original longer review. And this can help

you efficiently get a better sense of what

all of your customers are thinking.

Right. So that’s it for summarizing. And I hope that you can picture if you

have any applications with many pieces of text, how

you can use prompts like these to summarize

them to help people quickly get a sense of what’s in

the text, the many pieces of text, and perhaps

optionally dig in more if they wish.

In the next video, we’ll look at another capability

of large language models, which is to make inferences using text. For

example, what if you had, again, product reviews and you

wanted to very quickly get a sense of which product reviews have

a positive or a negative sentiment? Let’s take a look at how to do

that in the next video.