Large language models (LLMs) are highly effective for various writing tasks, including brainstorming, content generation, and translation, with prompt refinement being key to achieving specific and high-quality results.
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in the last video we looked at writing
reading and chatting as three major
categories of tasks that you can go to
all them to do given that large language
models were trained to repeatedly
predict the next word maybe it's no
surprise that they're pretty good at
writing at generating words and it turns
out that many writing tasks can be done
via web user interface so I hope you
find this video where will dive more
into writing tasks immediately useful
for writing task broadly what we to do
is start with a prompt and use a
relatively short prompt to write so to
generate a much longer piece of text so
let's take a look at some writing
applications I often use the web
interface of large language models as a
brainstorming partner if you ask it
brain some five creative names for
peanut butter cookies it actually comes
up with some pretty creative names noty
Nirvana Nevels I would eat that or if
you ask it to brainstorm ideas for
increasing sales then it comes up with a
few ideas and you can take a look to see
if any of these may be useful you can
also use a large language model again
maybe the web interface version to write
some copy for you let's start to an
example if you were to ask it to write a
press release announcing the hire of a
new coo a new Chief Operating Officer
for your company it may come up with a
piece of text like this company name
welcomes a CO's full name as so on and
so forth and this is a pretty generic
press release when writing a prompt you
find that if you can give the large
language model more context or more
background information then it will
write more specific and better copy for
you if all that the large language model
sees is this write a press release at
this point in time it doesn't know
anything about your company about the
new C's name or their qualifications and
so ends up writing some very generic
like this if you end up prompting a l
model like this is not a problem you may
realize that you wound up with a very
generic press release and decide to
update the prompt to give it more
information and serve you at a prompted
and say use the following information
for the press release this is a CO bio
this is the name of our company and some
details about our company then it would
write a much more detailed and
insightful press release specific to the
Coos joining this company I find that
when prompting an LM I'll often not get
the prompt right the first time like
what we saw just now where we had the
prompt R press release announcing the
new hire of coo without giving any
context and that's totally fine if you
see the result isn't what you want just
revise the prompt and try again I'll say
more about this in the later video this
week when we talk about tips for writing
effective prompts let's look at one more
example another writing task that I
sometimes use l for is translation in
fact some of the lar language models you
can access VI web UI are competitive and
sometimes even better than the dedicated
machine translation engines already
especially for languages with a lot of
text on the internet and so where the
large language model had a lot of data
to learn how to generate text in that
particular language it tends to do less
well in languages also called Low
resource languages with less text on the
internet in that language but if you're
operating a hotel and you want to
translate the welcome message into
formal Hindi to welcome guest then a
large language model may be able to
Output text like this for you um
unfortunately I don't speak Hindi I wish
I did but it turns out that this
particular translation is only so so the
word front desk it translates into the
desk at the front rather than you know
the reception which is what we mean when
we say the front Des of a hotel so if
you're working with a Hindi speaker and
I was when preparing the slide then they
able to give you some tips to say oh
this is some sort of not quite the best
formal Hindi but they were to tell it to
translate this into formal spoken Hindi
then it Updates this text to make front
desk translate into the Hindi word for
reception which is a much better
translation now here's one fun thing
I've seen recently in the AI Community
which is a lot of us that are working
with translation often need to translate
text into languages that we don't speak
ourselves so how can we tell if the
large language model is doing something
reasonable and in fact even if you have
say one Hindi speaker on your team if
other members of the team don't speak
Hindi how can they figure out what's
going on so what I'm seeing multiple
teams in AI Community do is translate
text into pirate English
for testing purposes and so if you were
to prompt Lish model to translate this
into pirate English you get Oho mate we
be hoping you relish your time B the
ocean view in that sounds a pretty good
pirate English to me so that howby grand
worthy models be used for writing let's
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