OpenAI-o1 x Cursor | Use Cases - XML Prompting - AI Coding ++
Summary
TLDRIn this video, the creator explores the use of OpenAI's GPT-3.5 and GPT-4 models on the coding platform, Cursor. They test different Cursor rules to enhance productivity and experiment with structured prompts using XML tags for clearer instructions. The video compares the efficiency of GPT-3.5 and GPT-4 for coding tasks, highlighting GPT-3.5's speed and reliability. The creator also discusses the benefits of GPT-4's larger output limit for complex refactoring jobs. They demonstrate using these models to create folder structures and automate website updates, showcasing practical applications and workflow improvements.
Takeaways
- ๐ The video explores the use of OpenAI's GPT-3.5 and GPT-4 models, focusing on the 'mini' version for coding within the Cursor platform.
- ๐ง The creator has been testing Cursor's AI rules to enhance productivity and is experimenting with XML tags to structure prompts more effectively.
- ๐ A Reddit post is discussed, comparing GPT-3.5 to GPT-4, with the conclusion that GPT-3.5 is currently superior for daily tasks due to its speed and reliability.
- ๐ The video highlights the 64k output token limit of GPT-4 as a significant advantage for large refactoring jobs, allowing more extensive code generation in fewer prompts.
- ๐ฌ The necessity for precise prompts with GPT-4 is emphasized due to its longer 'thinking time', suggesting that getting the prompt right the first time is crucial.
- ๐ ๏ธ Cursor's implementation of GPT-4 is noted to have some bugs, such as occasional failures to output text along with code.
- ๐ The video demonstrates using structured prompts with XML tags to generate complex folder structures and code, leveraging GPT-4's capabilities.
- ๐ The creator shares a workflow of switching between GPT-4 for initial setup and GPT-3.5 for debugging and fine-tuning.
- ๐ฅ A practical example is given where GPT-4 is used to automate the process of updating video content on the creator's website, showcasing a real-world application.
- ๐ The video concludes with a call to action for viewers to experiment with the models, share their findings, and consider integrating these techniques into their own workflows.
Q & A
What is the main focus of the video?
-The main focus of the video is to explore the use of OpenAI's GPT-3.5 and GPT-4 models, particularly the 'mini' version, for coding tasks using the Cursor platform. The video also discusses the effectiveness of different models for productivity and coding efficiency.
What is Cursor and how does it relate to the video's content?
-Cursor is a code editor that the video uses to test different coding rules and to integrate with OpenAI models for coding assistance. The video explores using Cursor to rewrite prompts with XML tags for more precise instructions when using AI models.
Why does the video mention XML tags in the context of coding?
-The video mentions XML tags as a method to structure and clarify prompts for AI models. By using XML tags, the video aims to provide more precise instructions to the AI, which can lead to better and more efficient code generation.
What are the advantages of using GPT-4 'mini' mentioned in the video?
-The video highlights the 64k output tokens of GPT-4 'mini' as an advantage, allowing for larger refactoring jobs to be done in fewer prompts due to the increased output limit compared to GPT-3.5.
What is the 'Chain of Thought' mentioned in the video in relation to AI models?
-The 'Chain of Thought' refers to the process where AI models think through steps to solve a problem, which can be time-consuming. The video suggests that with GPT-4 'mini', users need to provide more specific prompts to minimize waiting time for the AI's thought process.
What is the verdict on GPT-3.5 vs. GPT-4 'mini' in the video?
-The video concludes that for day-to-day coding tasks, GPT-3.5 is still considered better due to its speed and reliability, while GPT-4 'mini' is more suitable for large refactoring jobs that can benefit from its 64k output token limit.
How does the video suggest improving prompts for AI models?
-The video suggests improving prompts by using more structured language with XML tags to provide clear and detailed instructions to the AI, which can help in getting more accurate and efficient code generation.
What is the significance of the 64k output tokens in GPT-4 'mini' as discussed in the video?
-The 64k output tokens in GPT-4 'mini' allow for the generation of more extensive code in a single prompt, which is beneficial for creating large-scale project structures or refactoring without needing to split the task into multiple prompts.
How does the video demonstrate the use of GPT-4 'mini' for creating folder structures?
-The video demonstrates using GPT-4 'mini' to create complex folder structures by providing a detailed prompt wrapped in XML tags. It then uses the large output token capacity to generate all the necessary files and folders in one go.
What is the practical application shown in the video for automating website updates?
-The video shows a practical application where GPT-4 'mini' is used to create a script that can update a website's video section by generating a description based on a video title and URL, streamlining the process of adding new content to the site.
Outlines
๐ค Exploring OpenAI Models for Productivity
The speaker begins by discussing their exploration of OpenAI models, focusing on the 'mini' version. They mention testing different cursor rules to enhance productivity and experimenting with structured prompts using XML tags. The speaker also shares their weekend endeavor of seeking opinions on using GPT-3.5 for coding and reflects on the findings from a Reddit post, comparing GPT-3.5's speed and reliability with the newer models. They conclude that GPT-3.5 is still superior for daily tasks due to its consistency but remain open to exploring the 'mini' version's potential.
๐ป Leveraging Cursor and XML Tags for Coding Efficiency
In this segment, the speaker dives into their experience with using Cursor, an AI assistant, to improve coding efficiency. They discuss setting up rules within Cursor to suggest XML tags for clearer prompts. The speaker demonstrates how they've been using Cursor to rewrite prompts with XML tags for better clarity. They also show a practical example of creating a terminal app to fetch and display Hacker News posts, explaining the process of using structured prompts with OpenAI's 'mini' model and then switching to GPT-3.5 for debugging.
๐ Automating Folder Structure Creation with OpenAI
The speaker illustrates how they've been using OpenAI's 'mini' model to automate the creation of complex folder structures. They provide an example of a large project setup and demonstrate how to use the 'mini' model with its 64k output token limit to generate an entire project structure in one go. The speaker emphasizes the time-saving benefits of this approach and how it streamlines the initial setup of projects.
๐ ๏ธ Building a Video Update Pipeline for a Personal Website
In this part, the speaker shares their project of creating a pipeline to update their website with new videos. They outline the steps to automate the process of adding a video URL and title to generate a description and update their website's video section. The speaker uses a combination of OpenAI's 'mini' model for the initial setup and GPT-3.5 for fine-tuning. They successfully demonstrate the pipeline's efficiency and express their satisfaction with the time saved.
๐ง Final Thoughts on Integrating OpenAI Models into Workflow
The speaker concludes by reflecting on the integration of OpenAI models into their workflow. They express excitement about the potential of the 'mini' model for large tasks and the continued preference for GPT-3.5 for daily tasks. The speaker invites viewers to share their experiences and discoveries with OpenAI models and encourages experimentation. They also mention plans to continue exploring and possibly share their Cursor rules and prompts with the community.
Mindmap
Keywords
๐กOpenAI
๐กCursor
๐กXML tags
๐กProductivity
๐กPrompting game
๐กOutput tokens
๐กRefactoring
๐กComposer feature
๐กFolder structure
๐กDebugging
Highlights
Exploring the use of OpenAI's mini version with a focus on productivity and process improvement.
Utilizing XML tags and structure in prompts to enhance precision with AI models.
Testing different Cursor rules to optimize productivity.
Comparing GPT-3.5 with the new GPT-4 models for coding in Cursor.
GPT-3.5 is still considered superior for day-to-day tasks due to speed and reliability.
GPT-4's 64k output tokens allow for large refactoring jobs in fewer prompts.
The necessity for specific prompts with GPT-4 to avoid long thinking times.
Limitations of GPT-4 include the need for one-shot tasks and potential bugs in Cursor implementation.
Strategies for using GPT-4 for massive refactoring jobs in one go.
Using GPT-3.5 for smaller tasks and GPT-4 for larger, one-shot tasks.
Implementing Cursor rules to suggest XML tags for clearer prompts.
Demonstration of using Cursor to rewrite prompts with XML tags for clarity.
Creating a terminal app to fetch and display Hacker News posts using GPT-4.
Efficiently generating folder structures for projects using GPT-4's 64k output tokens.
Developing a pipeline to automate website video updates using GPT-4 and GPT-3.5.
Integrating the new pipeline into a personal website to streamline video updates.
Transcripts
today's video is going to be a bit of a
mixed bag we're going to continue trying
out open
ai1 uh focusing mostly on the mini
version uh today because we are playing
here over on cursor I've been testing
out different cursor rules to see if
that has any effect on my productivity
my processes and I've been kind of
trying to up my prompting game by using
more XML tags more structure and to do
that I've been trying to leverage cursor
here to actually help me rewrite my
prompts into this yeah you can see here
XML tags to hopefully get a more yeah
precise instruction so yeah I think
we're just going to dive in we have some
examples we're going to do so yeah let's
just get started so over the weekend
I've been also trying to look out for
what other people think of using let's
say 01 mini for coding uh maybe on
cursor so I found this Reddit posted
that I thought was pretty good and kind
of fits my view of 01 mini to after
trying it out over this weekend so I
thought we can just go through a few of
the points this uh who's afraid of 138
has made here I thought it had some good
points here so you can see the fast few
days I've been testing 01 mini uh in
cursor compared to 3.5 yeah which has
been a Workhorse model that has been
insanely consistent and useful that is
kind of my IDE too so the verdict is
claw 3.5 sonets is still a better
day-to-day model and that is kind of my
takeaway too just because of speed and
reliability and yeah that is my idea
right but I'm open to change right and
I've been trying out 01 mini over this
weekend so I just wanted to go through
some of the pros he found and some of
the cons and see if I agree here so the
64k output tokens uh that is big right
so if you compare I think a CLA 3.5
maybe has 8K output tokens so that's
eight times more output you can print
right in cursor so that is nice when you
do these composer features and you won't
like create bunch of files that we're
going to look at later uh so you can see
uh if your prompt is good it generally
can do a large refactor re architecture
job in two three shots yeah I think
that's good uh I'm not going to read
example so in general this was quite a
large of reflecting job and it do well
large output context is a big part of
facilitating this I think that's pretty
cool uh so here he has listed some cons
that I kind of agree with so you have to
be very specific with with your prompt
and that is kind of what I dived into
these XML tags this weekend to try to be
even more uh
instructive uh than I've been with clo
3.5 son to see if that can improve
something on when using 01 mini in
cursor right and you can see due to Long
thinking time you pretty much need a
perfect prompt because it's annoying
right if you don't have good enough
instructions and you waste time by
waiting for these 01 models to do their
Chain of Thought and and H there was
something wrong
right uh so he is working with 01 you
have to do everything one shot uh I have
not been doing that but let's see uh
limited chats yeah that's a huge limit
there of course but this is early 64
outcut that has to be that is not a con
right uh I wish son 3.5 has uh much more
output tokens yeah if Sonet had 64k that
would be pretty cool uh verbos yeah
cursor implementation is buggy this is
important so sometimes there's no text
output only code I've seen this too and
I wonder this if this is something to do
with um cursor
backend uh prompting I'm not quite sure
uh so I've been trying to do kind of my
own let me show you here I did this uh
let me see here yeah so I try this 01
rule so ignore all Chain of Thought step
by step prompting when using cursor
ignore chain of thought step by step
prompting me using cursor chat but I
don't know we might try it but I don't
think it's going to work to be honest so
we can see he has 01 mini CLA five son
conclusions so if you're doing a massive
refactoring job or gree and filling a
massive project use 01 mini combination
of deeper thinking and massive output
token limits means you can do things one
shot I haven't tried that too much so I
can't really reflect on that so if you
have a collection of smaller task CL
Sonet is still the king so be very
specific and overl Bose in your prompt
to1 mini describe as much as your task
in detail as possible it will save you
time because this this is not a model to
have conversations or fix small bugs
it's a Ferrari to the Honda at is Sonet
so yeah I've been switching models right
so I maybe I do like one shot with 01
mini and then I kind of switch to 3.5
Sonet so that's the way I've been
solving this but I just thought this
post was pretty interesting and it kind
of confirms
my uh comparisons with 3.5 Sonet 2 so I
just wanted to go through this because
it's ni to get like a confirmation of
what you've been feeling too right so I
think we're just going to dive over to
cursor now test some rules do some
prompting with XML text so let's just
head over there so if you see here on
cursor in the settings you see we have
something called rules for AI right and
here you can kind of set these general
rules that will apply for all your
sessions right but we can also include a
DOT cursor rules files if you didn't
know that and in this uh project we kind
of have these uh different rules so
always assist the user and suggest XML
rapper tags to improve the
prompt uh the user rights right and
we're going to the suggestions must help
must help the user make it as clear as
possible for an llm to understand the
scope of the prompt so these are the
rules I have been testing out and I just
wanted to show you kind of how I've been
playing around with this and how I've
been using cursor to kind of write my
prompts instead of doing it like yeah
just in the chat box right so if you're
planning to try this for yourself uh you
got to kind of look at down in the right
corner here there's something called uh
cursor tab so here we have a hacker
news. mark MD that's kind of a markdown
file so in this cursor tab here you can
see sometimes it says uh disable for
markdown uh so if this is kind of
checked off you can see we have this
cursor tab that is kind of not available
here so uncheck this so you can see we
can use this tab so you can see when I
start writing now right we get some Auto
suggestions right if you wanted to use
that if not just go back and just start
typing so let me just come up with a
prompt here and we can try to show you
kind of the way I've been playing around
with this okay so here you can see my
prompt now I want to create a terminal
app that fetch the top 10 posts on
Hacker News and display them in a retro
terminal style uh I want to scrape The
Hacker News website for this to post
using bs4 I want to use the rich library
to style it the terminal app must
include the links to the post and the
title of the post the code should be in
Python make the code modular easy to
understand and of course be effective
and secure as possible this I kind of
wrap in Project the action for llm is to
execute the project above okay so that
is a prompt I used to use right so what
I've been testing out here I just uh
select all contrl K so here I just go
wrap the prompt in XML tags to make the
instructions as clear as possible for
the llm I just do submit uh edit right
and we are on CL 3.5 Sonet here that's
fine and now this is going to rewrite
this it's going to add all the XML tag
it thinks is the best possible
instructions here to kind of get the
most out of that so you can see here
right so it right so let's just accept
this and take a look now so you can see
we have description we have description
requirements and we have some
requirements here kind of wrapped in
this XML tags we have action step by
step here right okay and yeah I think
this is fine so let's go ahead and try
this out now so let me just open our new
cursor here and let's test this prompt
using open ai1 mini I see I forgot to
add something I've been also focusing on
lately let me uh select all this and
let's do include our instruction to the
llm to include the folder structure of
the project with old folders and files
so this is also something I've been
playing around with lately because uh
using o1 we have this big uh 64k output
token so it's super easy to kind of
recreate folder structure using the
composer feature so you can see here now
uh we added a folder
structure I don't think I want it like
this so let me rephrase this prompt here
so let's do include a step to print a pH
structor with XML tags just the
instruction so I just wanted to add this
into the steps here right for Action
let's see if we can do that okay good
now we have it here so just accept that
let me save that and now let's try to
run this so I just open a new directory
here let's paste in our prompt right uh
now I want to select 01 mini uh because
these are very precise instructions
right so let's just try to run uh in the
chat here first if we get the Fage
structure we're going to take that and
we're going to go to the composer and
try to create all the files before we
add the code right okay that was pretty
quick so now you can see we have our
project structure so I'm just going to
copy this control I right paste in this
and I've kind of played around with uh
so this is kind of my instructions for
generating the folder structure so so
you're an L liit access to generating uh
folds and filers in the current working
directory generate all files and folders
so yeah we have some action here so I'm
just going to call copy this to head
back to our composer and just paste in
this so let's try this now so we are on
the 01 mini
right okay so you can see we have some
placeholder here we have the
requirements so let's just accept all
this and if you go back here now you can
see we have all our files right and then
we can just start adding here so we can
do requirements let's just add this
right we have our scraper dop good we
can apply the code here accept save we
have uh the display so now we have all
files ready right should be pretty easy
we have
main let add
this install the dependencies okay
install
requirements good and then we can run
it okay uh that was a bit strange you
can see it's running but we missing the
posts here uh so let's try to do like a
quick followup here and see if we can
fix this so I'm just going to go the app
is running but we don't see any hacking
News Post in our terminal but now I'm
going to select CLA 3.5 control enter to
kind of use the code base so this is
what I've been doing I've been switching
between 01 mini o1 preview and Claw 3.5
depending on the use case right so we
want to go to our main we want to add
some new code here right okay uh scraper
let's add some new code
here uh let's see now so let's clear
this let's run
it and boom we got it so here is our app
right top 10 Hacker News Post uh we have
this link we can follow right okay boom
we got it so that was pretty cool so
yeah seems to be working pretty nice uh
our F structure was good our initial
prompt from 01 mini was pretty good good
and then we switch to claw 3.5 for some
debugging and boom we got it so yeah so
yeah that was pretty good I'm happy with
this I just wanted to do one more
example to show you kind of how I've
been using 01 mini to create these
folder structures because that's been
pretty interesting right so here you can
see we have a huge folder structure
right and you can see here I kind of
took my prompt in uh in the end here so
you can see the action is create a
specific five Direct and files in the
current work directory for each file
include a basic placeholder content that
represents it purpose or structure so if
we grab this full prompt here so this is
a pretty big folder structure right so
if we grab all this uh let's head over
to yeah we can do just the same folder
here so let's head control shift I into
the composer let's remove this old one
and now you can see we paste in like the
full structure here and here is where
the kind of the big output window comes
uh 64k output token window comes into
play so you can see in the bottom left
here I guess you can't see because of my
face but we are selecting o1 mini and
when we run this now hopefully we can
create this big folder structure uh with
files and folders so let's see
now so you can see now it's writing all
the files right so this is pretty big
it's that's a lot of files to create
right and it was pretty fast when it
first got going here so if we accept all
this now right accept all we go back
here boom we got it so we have our back
end right uh with all the files
middleware chat controller Roots
Services open I service our front end
our app page layout Styles Global CSS
components right uh so what we had to do
now is just fill in this because we
already have created all the files we
could just since you can see they have
this placeholder code so if we had the
code ready now we can just apply this to
all the files so that means that we
didn't manually have to create all the
file Series this is a big Advantage I
have found with using 01 mini uh using
this composer just because of the big uh
output token window right or output
tokens so this is a bunch of files I'm
sure we can do even more if we try to so
I think this is a very good use case so
now if we want to chat we can just
switch to CL 3.5 and start adding code
here right I just think this is very
interesting and this is definitely
something I will be implementing into my
workflow so the final thing I thought we
can do today is actually do something
that is impacting me because this is a
feature I've been wanting for my own
website so let me just show you now so
this is the setup I have for my website
if we head over here now I have a
section called videos so here I kind of
upload all my latest videos right uh but
the way it's set up now if we we go here
I have to go into the code here right I
have to go to website I have to find my
Json structure down here
somewhere where is it yeah you can see
here uh I have to fill in this small Jon
here that has the title description and
iframe that is kind of the embedded
video you can see here and it's very
annoying to do this manually so I kind
of want to create a script that can just
maybe just link the URL and the title
and justun the script in the terminal
and this will generate this kind of
structure uh and I kind of want open a i
API to write the description based on
the the title right so let's see if we
can actually use cursor open a i uh1
mini I think and maybe some 3.5 to
actually create this uh pipeline for me
and this is kind of my raw prompt before
I add the XML tag so I want to create a
pipeline to update my react website it's
easier to use than the current method
now I have to go into the code in the
website to find a code where latest
videos are update the section uh I want
to add a nice pipeline I can use to
update the latest video section on my
website I want to be able to add a
YouTube video by copying the URL pasting
into a script that adds the video Title
by the
video into a script that adds the video
to the website the script should be able
to run with the following command so I
just want to add video URL and title to
write the description for the YouTube
video I want to use llm to generate a
short description based on the title uh
used open a i API the generated Json
structure must be fed into website JS to
update the website I pasted in some
documentation so that's the gp40 mini
implemented features above into my re
website project I will add website JS as
context so now I'm just going to select
all contrl K and let's wrap this into
XML tags so let's just wrap my prompt in
XML tags to make the task as clear as
possible so let's just try this and see
what structure we get now okay so let's
accept this and take a look here so now
we kind of have an objective we have a
current method okay we have the script
we want the process and the description
generation okay that's pretty good and
we have the implementation request so an
action and some context I think we just
going to try this and see if this
actually works so let me just copy this
head
over yeah to our uh directory here that
we have all the files so what I want to
do now is open up our composer right uh
I want to add the website as context I
want to add maybe like the folder so
that's going to
be Source I think and let's paste in our
prompt here now so I think we just going
to try this again I want to select 01
mini here for the first iteration so
let's run this and see where this takes
us okay okay so now you can see we have
our EnV file we have our videos Json so
this is kind of our where we're going to
store our examples if we look at the new
video section now we're going to load
from our Json file I think and the add
video script uh if we open up this I
think we're going to run this
command uh yeah we're going to include
our video and our title okay that's
interesting we need to install some
dependencies yeah I think this looks
pretty interesting and it looks pretty
good so what I'm going to do now is I'm
going to accept all this I'm going to
implement all the code I'm going to set
it up and then I'm just going to try it
and see how this actually work uh yeah
let me do that and I'll come back to you
when we are ready okay so now I kind of
want to show you how this works so if we
head to my website now uh you can see we
have this video here in latest videos
right so I just did a quick test looks
superb right so if we go here now you
can see uh we are using open AI gp40
mini to generate a concise and engaging
description from based on its title
right so we have some examples here on
kind of the ey framing right and yeah
I'm not going to go too much into the
code but this is going to be you can see
new videos will be inserted here by the
ad. video.js script perfect so how this
works now is we can do so what we can do
now node uh at video at video.js right
we can do D- URL and then I can head
over to let's say my YouTube channel uh
let's just copy this URL here I can go
back to cursor paste in this URL I can
do d-h title uh let's do a string and I
can go back here let's just what was it
copy this
title and paste it in here okay that
kind of missed so let me go back and fix
that and we need a string here uh okay
so I'm going to add this so generating
description using open AI boom okay so
you can see this got slotted in here it
got saved if we go to our website now
boom we got a second one so that was
super nice pipeline for me let me just
go grab another video here so AI app of
the week 2 I can just do node ad videojs
URL uh okay so I have the title boom
generate description add it here go back
to my we site boom we have another video
that saves me a lot of time so this is a
superb use case so I'm super happy with
this this is going to save me a lot of
time so yeah combining uh 01 mini with
kind of the The Prompt we created with
all the XML tags and we use some CL 3.5
for some simple
debugging how much time did I spend on
this that's maximum 10 minutes so that's
a superb integration into my website by
adding like a a new feature that saves
me a lot of time when updating new
videos so this is something I'm going to
just Implement and deploy right away so
that's pretty cool right uh yeah super
happy with this and the way it turned
out and combining all of these things
I've been playing around with this
weekend I think this yeah kind of helped
me with my productivity so I hope you
found some of these things interesting
uh if you want to access all of these
prompts I have here uh just follow the
link in the description I have kind of
open public repo where I will be putting
out all of these yeah the small prompts
here I've been using so just go check it
out if you want to copy maybe my folder
structure instructions or something like
that also if you want to shout out
something in my YouTube video some
GitHub project you have you can just go
to my website just go to Services I have
this YouTube video shout out I'm not
sure about the price yet so you can kind
of buy if you want to do some shout out
maybe on your GitHub repo other things
you want to shout out some small startup
you're working on just send me a request
here and maybe we can do something so or
I I can put out the cursor rules too if
you want to copy this but uh just go
play around try out different things
with 01 if you have access if you have
cursor you should have access to at
least a few uh requests right uh but I
think still there's a lot of things we
need to explore here when it comes to
using the new models uh but uh daily
driving is going to be CLA 3.5 maybe
some GPT 40 but I'm going to keep
experimenting using uh using the 01 pre
uh not not so much the o1 preview mostly
I'm going to be using 01 mini I think
but I'm going to try something with the
preview too and it was pretty cool to
see uh what other people have been using
on Min I've been watching some videos
this weekend there's a lot of cool stuff
out there uh but yeah I kind of agree
with this CLA 3.5 son it is still uh the
better day to-day model for me for
now uh but the 64k output tokens in 01
mini makes it super easy when we want to
do this uh big folder structure so that
is something I'm going to using it for
so I'm going to keep experimenting let
me know in the comments if you have any
cool things you have found out about
using o1 yeah I hope you took something
away from this video maybe you learned
something new maybe something you want
to try let me know in the comments give
it a like if you found it interesting
and yeah have a good day and we speak
soon
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