How to Fine Tune GPT3 | Beginner's Guide to Building Businesses w/ GPT-3

Summary

In this instructional video, Liam Motley guides entrepreneurs through the process of fine-tuning GPT-3 using NBA player performance data. He demonstrates how to prepare data, create prompt-completion pairs, and execute the fine-tuning process to build a customized AI model. The tutorial covers technical steps, including using Python scripts and the OpenAI API, and emphasizes the importance of understanding AI model fine-tuning for business opportunities in the AI industry.

Takeaways

• 📚 The video provides a step-by-step guide on how to fine-tune GPT-3 using specific data sets, such as NBA player performance data.
• 🛠️ Fine-tuning GPT-3 is a significant opportunity for entrepreneurs in the AI industry, allowing them to build businesses and applications tailored to their needs.
• 💡 Understanding the fine-tuning process is crucial for entrepreneurs looking to leverage AI models for business advantages.
• 📈 The script walks through the process of downloading and preparing data, creating prompt and completion pairs, and using them to fine-tune a model.
• 🔍 Data is manipulated in Google Sheets and then converted into a CSV format for use in the fine-tuning process.
• 📝 The script demonstrates how to use Python scripting to automate the creation of prompt and completion pairs from a CSV file.
• 🔑 An API key from OpenAI is required to access the fine-tuning functionality and to interact with the GPT-3 model.
• 📝 The video script includes a method to generate a Python script that automates the process of creating prompt and completion pairs, making it scalable.
• 🖥️ The script details the use of Visual Studio Code for editing and running the Python script that formats the data for fine-tuning.
• 🔧 The video mentions the use of a GUI for interacting with the fine-tuned model, allowing users to input prompts and receive responses.
• 🚀 The presenter emphasizes the importance of having a deep understanding of the fine-tuning process to stay ahead in the competitive AI industry.

Q & A

What is the main topic of the video?

The video is about the step-by-step process of fine-tuning GPT3 using a dataset, specifically NBA players' performance data, to create a fine-tuned AI model for entrepreneurial purposes.

Why is fine-tuning GPT3 considered a significant opportunity in business?

Fine-tuning GPT3 is seen as a significant opportunity because it allows entrepreneurs to leverage the power of advanced AI models to build on top of them and create valuable businesses.

What is the first step in the fine-tuning process as described in the video?

The first step is to find a set of data, in this case, NBA players' performance data, which will be used to train the GPT3 model.

How is the data manipulated before being used for fine-tuning?

The data is imported into Google Sheets, where unnecessary rows are removed, filters are applied to remove blanks, and the data is formatted into a CSV file ready for processing.

What tool does the video suggest using to visualize CSV files more easily?

The video suggests using a tool called 'Rainbow CSV' to visualize and understand the structure of CSV files more easily.

What programming language is used in the script to generate prompt and completion pairs?

Python is used in the script to generate prompt and completion pairs from the CSV data.

What is the purpose of the script provided by the video?

The script automates the creation of prompt and completion pairs from the CSV data, which is necessary for the fine-tuning process of GPT3.

What is the significance of the 'Max tokens' parameter in the completion creation process?

The 'Max tokens' parameter is a safety feature to limit the amount of text generated in a response, which in turn helps to control the usage and cost associated with the API.

Why is it necessary to have a unique suffix in the completions during the fine-tuning process?

A unique suffix helps differentiate the fine-tuned model's outputs from other texts, ensuring that the model's responses are correctly identified and processed.

What platform is used to interact with the fine-tuned GPT3 model after the process is complete?

A graphical user interface (GUI) is used to interact with the fine-tuned GPT3 model, allowing users to input prompts and receive responses.

What is the recommended model to use for fine-tuning according to the video?

The video suggests retraining with the 'DaVinci' model for better text recognition capabilities, after initially training with the 'Curie' model.

What advice does the video give for entrepreneurs looking to understand AI fine-tuning processes?

The video advises entrepreneurs to invest time in understanding the fine-tuning process to gain a competitive edge, identify data sources, and integrate them into AI models like GPT3 or the upcoming GPT4.

Outlines

00:00

📚 Introduction to Fine-Tuning GPT3 for Entrepreneurs

This paragraph introduces a video tutorial focused on fine-tuning GPT3 for beginners, especially entrepreneurs interested in AI. The speaker plans to demonstrate the step-by-step process of fine-tuning a model using NBA player performance data. The tutorial aims to provide a basic understanding of the fine-tuning process, enabling viewers to apply this knowledge to their AI ventures. The speaker emphasizes the importance of understanding these processes to identify opportunities and build valuable businesses in the 'AI Gold Rush'.

05:02

🔍 Step-by-Step Guide to Fine-Tuning with NBA Data

The speaker provides a detailed walkthrough of the fine-tuning process using NBA players' performance data. The process begins with downloading the dataset and manipulating it in Google Sheets to prepare it for fine-tuning. The data is then formatted into prompt and completion pairs, which are necessary for training GPT3. The speaker also discusses the creation of a Python script to automate the generation of these pairs, ensuring scalability. The paragraph concludes with the execution of the script to produce a JSON file containing the prompt and completion pairs.

10:02

🤖 Fine-Tuning Execution and Model Interaction

This paragraph details the execution of the fine-tuning process, starting with obtaining an API key from OpenAI and installing necessary libraries. The speaker explains how to prepare the data in JSONL format and fine-tune the model using a specific base model. After the fine-tuning process, a unique model name is provided, which can be used to interact with the model through a graphical user interface (GUI). The speaker encounters an issue with the completion output but identifies a fix by adjusting the 'Max tokens' parameter. The paragraph concludes with the speaker retraining the model with a different model for improved results and demonstrating the interaction with the fine-tuned model.

🚀 Conclusion and Encouragement for Entrepreneurs

The final paragraph wraps up the video by summarizing the process of fine-tuning a GPT3 model with a dataset and emphasizes the importance of understanding this process for entrepreneurs. The speaker encourages viewers to explore the provided resources, such as the Google doc with prompts and code, and to engage with the community for further learning. The speaker also highlights the potential of fine-tuned models to answer specific questions about the dataset and suggests that with more data, the model's understanding can become more flexible. The video concludes with an invitation to subscribe for more content on AI entrepreneurship and a reminder of the ongoing opportunities in the AI industry.

Keywords

💡Fine-tuning

Fine-tuning refers to the process of adapting a pre-trained machine learning model to a specific task by retraining it on a smaller, more focused dataset. In the context of the video, fine-tuning is applied to GPT-3, an advanced language model, to specialize it in understanding and generating content related to NBA player performance data. The script illustrates this by showing how to take a dataset and use it to train the model to produce specific outputs.

💡GPT-3

GPT-3 is a state-of-the-art language model developed by OpenAI, capable of generating human-like text based on the input it receives. The video's theme revolves around customizing GPT-3 through fine-tuning so that it can generate specific insights about NBA players. The script demonstrates the process of interacting with GPT-3 to prepare data and generate prompting completion pairs for fine-tuning.

💡Prompting Completion Pairs

Prompting completion pairs are sets of input-output examples used to train a language model. In the video, these pairs are essential for teaching the fine-tuned GPT-3 model how to respond to specific queries about NBA players. The script includes a method to programmatically generate these pairs from the NBA dataset, showing how to format them for training the model.

💡NBA Players Performance Data

NBA Players Performance Data refers to a dataset containing statistics and information about the performance of basketball players in the National Basketball Association. The video uses this dataset as the basis for fine-tuning GPT-3, aiming to create a model that can provide detailed statistics and summaries about NBA players. The script demonstrates how to manipulate this data to create prompting completion pairs.

💡CSV Format

CSV stands for Comma-Separated Values, a widely used format for spreadsheets and databases that stores data in plain text with commas separating each field. In the video, the NBA performance data is initially in CSV format, which is then manipulated and used to create prompting completion pairs for the fine-tuning process. The script shows how to read and process this CSV data.

💡JSON

JSON, or JavaScript Object Notation, is a lightweight data interchange format that is easy for humans to read and write and for machines to parse and generate. In the context of the video, JSON is the format used for the prompting completion pairs after they are generated from the CSV data. The script includes a step to convert the data into JSON L (JSON Lines), which is suitable for the fine-tuning process.

💡API Key

An API key is a unique code used to authenticate requests to an API (Application Programming Interface). In the video, the presenter mentions obtaining an API key from OpenAI to access their services for fine-tuning the GPT-3 model. The script includes steps to securely use this key within the program to interact with the OpenAI API.

💡Entrepreneur

An entrepreneur is an individual who creates a new business, bearing most of the risks and enjoying most of the rewards. The video is aimed at entrepreneurs interested in AI and how to leverage fine-tuning processes to build businesses in the 'AI Gold Rush'. The script emphasizes the importance of understanding the fine-tuning process for entrepreneurial opportunities in AI.

💡AI Gold Rush

The term 'AI Gold Rush' is used in the video to describe the current era of rapid development and commercial opportunity in artificial intelligence. The presenter suggests that understanding and utilizing AI technologies like fine-tuning GPT-3 can provide significant advantages to entrepreneurs in this competitive landscape.

💡Data Manipulation

Data manipulation involves altering or transforming data to fit a specific purpose or format. In the video, data manipulation is shown through the process of cleaning and formatting the NBA player performance data in Google Sheets before converting it into prompting completion pairs. The script details the steps taken to filter out blank entries and prepare the data for fine-tuning.

💡Python Scripting

Python scripting refers to writing code in the Python programming language to automate tasks or process data. The video demonstrates the use of Python scripting to generate prompting completion pairs from the CSV data and to interact with the OpenAI API for fine-tuning the GPT-3 model. The script provided is a practical example of Python's capabilities for data manipulation and API interaction.

Highlights

Introduction to a step-by-step video on fine-tuning GPT3 and building an AI startup.

Explanation of the necessity for entrepreneurs to understand fine-tuning processes in the AI gold rush.

Starting with NBA players' performance data set for the fine-tuning example.

Importing and manipulating data in Google Sheets to prepare for fine-tuning.

Downloading the data in CSV format for further processing in Visual Studio Code.

Using Chat GPT to generate a script for creating prompt and completion pairs.

The process of string interpolation and F-strings in Python for automating data processing.

Installing and using the rainbow CSV extension for better CSV visualization.

Running the Python script to generate prompt and completion pairs in JSON format.

Using OpenAI's API key for accessing fine-tuning capabilities on beta.openai.com.

Preparing data in JSON L format for the fine-tuning process.

Initiating the fine-tuning process with a specified base model and data file.

Creating a GUI for interacting with the fine-tuned GPT3 model.

Troubleshooting the GUI for displaying the entire completion from the fine-tuned model.

The importance of training with a DaVinci model for better text recognition.

The potential of fine-tuned models to answer specific and complex questions with enough training data.

Final thoughts on the importance of understanding fine-tuning for entrepreneurs in the AI industry.

Resources provided in the video description for further learning and experimentation.

Encouragement for viewers to subscribe and engage with the channel for AI entrepreneurship content.

Transcripts

my recent video about how to fine-tune

gpt3 and build an AI startup in a few

minutes got a lot of questions about the

Nitty Gritty details on how you can

actually do this fine tuning process

rather than try to answer you all in the

comments I thought I'd pop back on here

and make a quick video showing you the

step-by-step process of how to go from

data to prompting completions to a fine

tune model that you can interact with

I'm going to be breaking it down a lot

more granularly in this video and going

A to Z on how any beginner can come in

and use data to fine tune it and get a

fine-tuned version of gpt3 to use for

their own purposes if you're an

entrepreneur and are looking to start

making money and building businesses in

the AI Gold Rush you need to be aware of

how these fine-tuning processes work so

this video is exactly what you're going

to need to learn to get to a basic level

of understanding so that you can move

forward and start hiring people and know

what the process is and be alert and

aware of what's going on so we're going

to jump back in here where we left off

with the NBA players performance data

set I'm going to do this all again so

we're starting off by downloading the

data set links to this will be in the

description below fine-tuning these

models is one of the biggest

opportunities in business of 2023 and

I'd say for the next few years because

of the power of these models and being

able to build on top of them one of the

most powerful skills that you can have

as an entrepreneur right now is

understanding how this process works so

that you can start to see opportunities

way you can apply data to these models

and build a valuable business so don't

go anywhere I'm going to be going step

by step explaining this whole process

and you're not going to want to miss any

of it enough talk let's get stuck into

it a quick overview of this process from

start to finish to start off we need to

find a set of data which in this case

we're using the NBA players performance

started from my previous video then

we're going to take that data and create

prompt and completion pairs which is the

format that we need to provide to gpt3

in order to fine tune it and finally

we're going to be able to run a basic

app on our computer that is a fine-tuned

version of gpt3 that understands the

starter that we fit to it to start off

we're going to download the NBA player

performance data from kaggle the dataset

link will be in the description next

we're going to import the data to Google

Sheets so that we can manipulate it a

bit we have an index row here on the

left that we can remove

now I'm just going to put a filter on

this so that we can remove the blanks as

I did in the previous video

copy this

create a new sheet and paste it in now

we have it all formatted nicely and

Compact and ready to go we're just going

to download this data in a CSV format

and then open it in Visual Studio code

so we can have a look at it now what you

need to do is you don't have to visual

studio code or your favorite code editor

what I'm going to do here it's already

opened up but just to help you guys out

I'm going to actually close that and

then show how you open it so I'm going

to go open folder

and here is the folder that I've created

you need to create a new folder anywhere

on your desktop will work and then you

need to open that folder by clicking

open here and then inside it I've got

the I've dragged in the CSV file that we

just downloaded and in here's also some

of the uh all the data that we download

from the kaggle set which we don't

really need at the moment because we've

got the one that we need which is the

scoring then you're going to want to

copy the header row and take it over to

chat GPT

and then we're going to give it just an

example row of data so it kind of knows

what format works I'll give it a couple

actually let's give it four rows of data

to have a look at so this is the prompt

I'm ready to get started I have a

spreadsheet of basketball data these are

the column headings in CSV format and

I've pasted in the column headings and

I've gone here are the rows of data in

CSV format and pasted in a few example

rows for it to use finally I'm asking it

do you understand and I'm hoping after

this prompt is sent it's going to say

okay I understand what you're trying to

do here I've read the CSV and I

understand the format of the file great

tattoo BT is understood what we've put

into it and now we need to ask it for

some prompting completion pairs the

method I'm going to show you here is

actually different to the one I did in

my previous video in my last video the

way I showed you how to generate these

promptly completion pairs using chat GPT

is actually not that scalable so I've

done a little bit of research and

figured out how to get chat GPT to write

us a script to generate these pairs and

so we can do that with like hundreds of

thousands of different pairs so we're

going to get stuck into that now I'm

telling chat GPT that I want to

fine-tune gpt3 using the starter write

me a script to create prompted

completion pairs within this format and

then pasting some examples of the format

that I want it back in I'm actually

going to insert python in here just to

be extra clear okay now after a bunch of

missing around and tweaking this just to

get it just right so you only have to do

it once this is the exact prop that you

guys will need to put in I had to really

coach it in order to give the right

script out the first time so that you

guys didn't have to mess around with it

so if you're following along head down

below to the description I'm going to be

pasting these prompts into a Google doc

and leaving the share link for you guys

to check out down below so if you want

to get this entire prompt here that I've

had to tweak quite a lot and you can

follow along then just kid down below

and you can get it off Google doc after

submitting that prompt it gave me back

the script that I need to get this data

into the promptly completion pairs so

once you have this code all you need to

do is copy it and head back over to vs

code right click in here and create a

new file and we're going to call it

main.pi

paste this in save the file and then

you're going to want to make sure up

here this basketball data.csv is the

same name as this so you can just click

on here press enter paste it in there

and save the file so now we have the

name thing all ready to go and then we

have it the script all ready to go as

well if you're not too familiar with

programming and and this basic sort of

python scripting I'm going to give you a

quick run through of what's Happening

Here so that you're not completely blind

here we have our CSV data and this is

comma separated value so each one of

these at the top you have the header and

a comma separates it to the next column

so this is basically a condensed version

of a spreadsheet that a computer can

read we have all of the headings up here

and they're separated by commas so the

computer can read along and say okay

this is a column and I've got a uh

extension installed on this computer

that allows me to see these a lot more

easily I'm actually just going to show

you that now

and here it is it's called rainbow CSV

if you just install this quickly then

it's going to help you visualize the CSV

a lot more easily like I am here so it's

pretty straightforward we have the

header row and then underneath it is

each of the data points for that same as

a spreadsheet but it's just not

formatted as nicely so by using a method

called string interpolation and F

strings as you can see here this f means

that anything that you put inside of

these curly brackets here is going to be

uh the value of this player here is

index player so this is referencing the

player column here right at the end so

this player one when you see that key

player it's referencing that in the

script here so for every row of data

that we have coming along here it's

going to take this player column which

is the green thing here and it's going

to say okay I'm gonna because we're on

this row and it's going to work down

every single row through the whole sheet

it's going to take the player value

there

and write the prompt with it write a

summary of player values statistics and

then it's going to start building the

completion for that row and once again

it inserts the player's name and it says

player name played games played games

starting game started of them so it

writes a big long sentence and summary

of what the player's data is and every

time it comes to one of these blocks

here which has got the curly brackets

it's reaching into this file and

plucking out the right value as the

script has made it then all it needs to

do is append these prompting completion

pairs together into the format that we

asked it for back in chat GPT here which

is this format

and then it just saves it to a Json and

dumps it out for us to look at if you're

following along I'm assuming you've got

python installed on your machine if not

head to their website and you can

download the latest release of python

now that you understand the script let's

actually run it and then see what we get

the command to run a script and platform

is Python and then the name of the

script so this case is main.pi and hit

enter and then just like that we have a

prompt and completion peers.json which

looks like a complete mess but it is a

ton of data all formatted exactly how we

wanted it if you'd like to see it a

little bit more pretty what we can do

here is type our pritia

and this one here if you just install

this quickly and then you head back to

your uh prompt and completion pairs you

can press option shift and if and then

it will format it all up nicely like

this it looks really good and actually

color codes and understand it as Json so

you're actually probably going to need

to do this so head over grab that

extension and come back and press option

shift and F now we can see the result of

our hard work so now if every player on

that spreadsheet that we started with we

have the prompt which is write a summary

of Luca don't shoot your statistics and

in the completion which uses check TPT

summary structure and then it's simply

done it programmatically and use string

installation which is a python feature

to pluck data out of that CSV file and

put it into the correct place to create

this completion I've just taken all of

the code out of the main.pi and put it

into a new file called generate.pi and

saved it so that we can play around in

the main.pi file for the next step

another fun part of taking all of these

pairs and funneling them into gpt3 and

fine-tuning it begins to get started

you're going to want to head to

beta.openai.com and you're going to want

to head over to your personal section

and view your API key to create an

account if you haven't already it's free

and then you're going to want to create

a new API key here I am just creating a

new one now you can see all that on

screen but I'm going to delete it you

cheeky guys in the in the comments I'm

just going to put that there to save it

for later now we're on the documentation

page for fine tuning bio open AI so

we're going to head down to the

installation we're going to copy this

and head over to our terminal and paste

it in

I've already got everything downloaded

of course so the requirements are

already satisfied for me but it should

start installing and show a progress bar

for you now you need to copy this export

string and just bring it back over to

your main.pi file to copy this entire

API key and then paste it within these

quotations

now we need to copy this entire thing

hit back and paste this in and this

means that it's worked we've exported

the open AI key so now it's ready to use

for later next we're going to have to

prepare our data so you can copy this

head over to your terminal and delete

all this and this local file means we

need to reference our content completion

file and what we can do is come over

here copy all of this head back paste it

in there and hit enter I've just taught

it to prepare my data and it says here

your file appears to be in Json will be

converted to Json L which is the format

it needs your file contains 250 prompted

completion pairs which is a pretty good

starting little batch of data we have

here now it gives you me a whole bunch

of tips and tricks on how it can make

the data better and get better results

out of it so you probably want to have a

read through this whenever you do this

again and uh follow all of the

instructions here because it's going to

make your model a whole lot better for

all of these pairs we should really have

a suffix on it that is really unique and

like a bunch of slashes and hashtags and

stuff too there's a few things here like

starting all of your completions with

the white space character using a unique

ending like pound signs on the end of

your completions all of these are really

important to do we don't have time in

the scope of this video if you'd like a

little bit more on that I can shoot a

quick loom for you guys and put it in

the in the comment section below but for

all purposes of this video we're good to

go and we can hit in on this and it's

going to convert it from Json into Json

l

and again add a white space character to

the beginning of the completion it's

going to do it for us which is great

and yes

and just like that we have it all made

up into Json L format ready to put into

the fine tuning process now we can

actually fine tune our model uh you need

to head over here and copy this and note

that you can change the name of the base

model that you're starting from so I've

got this put in here open AI fine

tunes.create I've put Curie at the end

to specify the model that we want to use

and now I also need to put in the path

to the file which is all the data that

it's going to use to fine-tune so I've

got to go over here copy this entire

thing including the suffix and then

paste this in here and then hit enter so

what this is now going to do is upload

all of that data and put you in the

queue to fine tune and then it's going

to put all that data through their fine

tuning method and then the result is

going to be a fine-tuned version of gpk3

that is familiar with all of this

basketball information and in just a few

minutes we've got our fine tune model

complete and here down on the bottom of

the screen you can see what the name of

the model is called you're going to want

to copy that and save it for later now

we need to head back to chat gbt so that

we can get a graphical user interface or

GUI so that we can interact with this

now to make things super easy I'm

actually just going to grab the GUI

script from the previous video that I

did so I'm going to copy this this is

going to be available in the Google talk

down in the description so head over

there and grab that this is going to

have all this code that you need in

order to run a basic GUI so that you can

interact with your fine tune model and

we're going to head back and here we

have the name of our model that we want

to cut that out of there and replace it

within these uh quotations here now

before you try to start this app up make

sure you save your main.pi file before

you start running it python main.pi

function ran and just like that we have

a fine tune gpt3 window up and we can

start giving it prompts if I paste in

one of these prompt here Jason Tatum

statistics and paste them in it's going

to give me out what is the beginning of

this completion now it seems to be

having an issue where it's not writing

out the entire completion I'm not sure

if that's an issue with my API key or a

limit on the API request or it's an

issue within the GUI itself but I'm

going to have a dig into that over the

next couple hours and get back to you

guys in a bit hey guys I just took a

little look into why it's not completing

the rest of it and turns out it's a

pretty simple fix in the completion line

here

openai.completion.create there's

actually a parameter that is a Max

tokens that is usually set so 10 or

something just so that it limits how

much you're charged through the API so

it's a built-in safety feature to stop

you spending too much money all you need

to do is come in and change us here by

adding in this comma Max tokens equals

I've done 150 and that about that's

about right for what we need so if I

just run the Python program again here

bring it over another thing you need to

note as well is that in the preparing

process I didn't notice it at the time

but what it did is trim out the writer

summary part so as you can see here it

says write a summary of look Advantage

statistics but what it did is because it

was shared with all of the different

prompts it actually cut it out in the

preparation process so all our prompts

are really is just their name and their

statistics afterwards so if I take that

over to our app here and put it in load

for a bit now I've actually retrained

this on a DaVinci model off camera just

because DaVinci is actually a lot better

for recognizing what text you want so if

you train with Curie make sure you go

back and retrain with DaVinci it cost me

about three bucks in order to get it

retrained but definitely worth it and as

you can see here we've got the entire

prompt here which we expected and it's

actually started giving us information

on Karl Anthony towns and statistics so

I'm not 100 sure why it's continuing

down the list there but we got our

result we got our entire print out of

janus's Statistics so I've been a result

the last episode thanks for sticking

around and we'll get back to the video

for the purpose of this video I've shown

you how to go from start to finish how

you can take your data you can prepare

that data you can get it put into a

promptly completion Pairs and then

finally fine-tuned your version of gpt3

so that you can start interacting with

it of course this is an extremely basic

example and the understanding that this

gpt3 fine-tuned model has of this

basketball topic is very limited and you

need to give it uh probably thousands

and thousands more variations of these

prompts once you've given it enough data

its understanding will be flexible of

the topic and you'd actually be able to

ask us basically any question and start

being sort of specific about it and

saying hey what are the three players

playing for the XYZ team who have the

highest field goal percentage and that's

the kind of stuff that you'd eventually

be able to get to given enough training

data so that's all for the video guys

I've shown you how to go from start to

finish and fine tuning a model with a

bit of data so if you have any questions

about this or you've got stuck or

something's not working on your computer

Drop it Down Below in the comments

either I'll help you or someone else in

the community will the important thing

about learning this process is that as

an entrepreneur you need to understand

that this is what is going on behind the

scenes for a lot of the startups that

you're seeing just spending half an hour

or an hour trying to understand this

process is going to put you leagues

ahead of other entrepreneurs and other

people trying to make money in this AR

gold rush because you understand the

underlying buying technicalities of how

these models are being created and

fine-tuned but the understanding of this

process you are going to be keeping a

close eye out for data sources and

understanding how you can get that data

source and integrate it into a gpt3

model or a gpt4 model which is coming up

very soon so I hope you got something

out of this remember that down in the

description I'm going to have a Google

doc having all of the prompts that I

sent to church EBT and also the code so

it should be pretty straightforward for

you guys to have a play around with us I

really hope you enjoyed and got

something out of it if you like content

like this my name is Liam Motley and I'm

a self-made serial entrepreneur from New

Zealand but now I'm living in Dubai I

make AI entrepreneurship focused content

at least three times a week for aspiring

and established entrepreneurs looking to

get into the AI industry and make money

in this hour Gold Rush it's happening

right in front of us so if that kind of

stuff sounds interesting to you hit down

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your comments down below and I'll be

answering as many as I can that's all

for today thank you so much for watching

and the best of luck to you as you

navigate This Hour Gold Rush I'll see

you next time