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Thank you for using OpenAl!
These Terms of Use apply to your use of ChatGPT, DALL-E, and OpenAl’s other services for
individuals, along with any associated software applications and websites (all together,
“Services”). These Terms form an agreement between you and OpenAl, L.L.C., a Delaware
company, and they include our Service Terms and important provisions for resolving
disputes through arbitration. By using our Services, you agree to these Terms.
If you reside in the European Economic Area, Switzerland, or the UK, your use of the
Services is governed by these terms.
Our Business Terms govern use of ChatGPT Enterprise, our APIs, and our other services for
businesses and developers.
Our Privacy Policy explains how we collect and use personal information. Although it does
not form part of these Terms, it is an important document that you should read.
Who we are


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pageType: Marketing Marketing Marketing Marketing Marketing Marketing Marketing

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Building momentum with ChatGPT Enterprise
With the launch of ChatGPT Enterprise, Moderna had a decision to make: continue
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Enterprise?”
As a science-based company, we research everything,” said Brice Challamel, Head of Al
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comparing mChat, Copilot, and ChatGPT Enterprise. “We found out that the net promoter
score of ChatGPT Enterprise was through the roof. This was by far the company-favorite
solution, and the one we decided to double down on,” Challamel said.
Once employees had a way to create their own GPTs easily, the only limit was their
imaginations. “We were never here to fill a bucket, but to light a fire,” Challamel said. “We
saw the fire spread, with hundreds of use cases creating positive value across teams. We
knew we were on to something revolutionary for the company.”
The company’s results are beyond expectations. Within two months of the ChatGPT
Enterprise adoption:
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Overview For Everyone Safety overview About us Terms of use
Index For Teams Safety standards News Privacy policy
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|Moderna partners with OpenAl to deploy ChatGPT Enterprise to thousands of employees
across the company. Now every function is empowered with Al, creating novel use cases
and GPTs that accelerate and expand the impact of every team.
Moderna has been at the intersection of science, technology, and health for more than 10
years. Moderna’s mission is to deliver the greatest possible impact to people through
mRNA medicines—with the COVID-19 vaccine being their most well-known breakthrough.
The company has partnered with OpenAl since early 2023. Now, ChatGPT Enterprise is
evolving how Moderna operates across each function.
Moderna is using its platform for developing mMRNA medicines to bring up to 15 new
products to market in the next 5 years—from a vaccine against RSV to individualized
cancer treatments. In order to achieve its ambitions, Moderna has adopted a people-
centric, technology-forward approach, constantly testing new technology and innovation
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safe and beneficial Al.
OpenAl's charter >  Our approach to alignment research >
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The major design figures in UX design
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Don Norman
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specialist to use this term in the field of UX.
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Cooper has made significant contributions, including
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As UX design evolved, new figures emerged that helped
build this field. Some of the major contributors to UX design

include:
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as Nielsen's heuristics) that guide how we create user-
friendly interfaces.
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Cooper has made significant contributions, including
introducing the concept of personas.
Tip! Discover the essence of Nielsen’s heuristics and learn
how to apply them to your designs by taking our
10 Usability Heuristics by Jacob Nielsen lesson.

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earc
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9] GET STARTED
@ Text generation % Prompt engineering
= Overview Learn how to generate text and call functions Learn best practices for prompt engineering
{=} Introduction
8 Quickstart Embeddings n Speech to text
& Models Learn how to search, classify, and compare text Learn how to turn audio into text
Tutorials
Image generation Fine-tunin
@ Changelog E] 9¢9 . z 9 i
@ Learn how to generate or edit images Learn how to train a model for your use case
| CAPABILITIES
i 0 Text to speech ® Vision
Text generation 7 Learn how to text into spoken audio Learn how to process image inputs with GPT-4
Function calling
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Eine-tuning Build an assistant
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® Introduction Beta Assistants deep dive Beta
Vision & Learn the basics of building an assistant Explore how assistants work and important concepts
Text-to-speech
Speech-to-text .
Watch the first OpenAl Developer Day keynote
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ASSISTANTS OpenAl DevDay: Opening Keynote
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e Overview . .
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Q CAPABILITIES Make your first Chat Completions APl request Explore what OpenAl models can do with prompts
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GPT-40 New GPT-4 Turbo GPT-3.5 Turbo
Vision
Text-to-speech Our fastest and most affordable Our previous high-intelligence Our fast, inexpensive model for
flagship model model simple tasks
Speech-to-text
Viod < Text and image input, text output < Textand image input, text output <$ Textinput, text output
oderation
£ 128k context length £ 128k context length £ 16k context length
B Input: $5 | Output: $15* B Input: $10 | Output: $30* B Input: $0.50 | Output: $1.50*
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Develop safe, beneficial Al systems
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38 Explore GPTs
ChatGPT starter guide Tell me a fun fact
Create a morning routine Help me pick
2% Invite members
@ Message ChatGPT
John Doe
" Mobbin.com "
Mobbin.com workspace chats aren't used to train our models. ChatGPT can make mistakes. .
|® chatGPT @  ChatGPT4 v
38 Explore GPTs
ChatGPT starter guide Tell me a fun fact
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" Mobbin.com "
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Switching chat model

screenKeywords:
@ @& ChatGPT4o S Q)
balance += winnings
free_spins > O:
( )
winnings free_spins:
( )
balance -= bet
balance > O:
play_again = ( )
( )
( )
__hame__ == H
main()
Explanation:
e Spinning Reels: Now spins a 3x3 grid of symbols.
¢ Paylines: Checks multiple paylines including horizontal and diagonal.
e Special Symbols: 'Wild' can substitute for any symbol to complete a win; 'Scatter' triggers free
spins.
¢ Payouts and Bonuses: Adjusted for wins on different lines and free spins bonuses.
You can further develop this with a graphical interface using Python libraries like Pygame to make
the game visually appealing and interactive.
< 2/2 g S <
Mobbin.com workspace chats aren't used to train our models. ChatGPT can make mistakes. ?
|@ @& ChatGPT4o S Q)
__nhame__ == :
main()
How the Code Works:
1. Symbols: The symbols list has been updated to ["Mountain”, "Tree", "Lake", "Fish", "Boat", "Bird"]
to match the mountain lake theme.
2. Game Messages: The welcome and thank you messages have been updated to reflect the
theme.
3. Winning Logic: The winning logic remains the same, where matching three symbols results in a
win.
You can further enhance the theme by adding more visual elements if you develop a GUI version of
the game using libraries such as “tkinter" or “pygame . However, for a simple console-based game,
this implementation should give you a good starting point.
@ bank_support_train.csv
Based on those dataset, make a more complicated game
&) To develop a more complex slot game using the theme from the serene mountain lake landscape
image, we can introduce features like multiple paylines, bonuses, and special events that are
triggered based on certain combinations. Here's an idea for expanding the game:
Advanced Slot Machine Game Features
1. Multiple Paylines: Instead of just one horizontal line, add vertical and diagonal paylines for more
chances to win.
Mobbin.com workspace chats aren't used to train our models. ChatGPT can make mistakes. ?
|@ @& ChatGPT4o S Q)
winnings > O:
( )
balance += winnings
( )
balance -= bet
balance > O:
play_again = ( )
( )
( )
__hame__ == :
main()
Explanation:
1. Symbols: Symbols are extracted from the dataset's 'job' and 'marital’ columns. Modify this as per
the actual dataset columns available.
2. Reels:” nbols.
GPT-40
4 v
3. Payline M »r winning combinations.
4. Winnin GPT-4 atching symbols in any of the paylines.
+
Run the abc nt after inspecting and adjusting the dataset's
columns an 4 GPT-3.5 ( 3Yyouamore complex and engaging slot machine
game.
P S o
Mobbin.com workspace chats aren't used to train our models. ChatGPT can make mistakes. ?
|@ @& ChatGPT4o S Q)
balance > O:
play_again = ( )
( )
( )
__nhame__ == :
main()
How the Code Works:
1. Symbols: The symbols list has been updated to ["Mountain”, "Tree", "Lake", "Fish", "Boat", "Bird"]
to match the mountain lake theme.
2. Game Messages: The welcome and thank you messages have been updated to reflect the
theme.
3. Winning Logic: The winning logic remains the same, where matching three symbols results in a
win.
You can further enhance the theme by adding more visual elements if you develop a GUI version of
the game using libraries such as “tkinter" or “pygame . However, for a simple console-based game,
this implementation should give you a good starting point.
@ bank_support_train.csv
Based on those dataset, make a more complicated game
e ©
Mobbin.com workspace chats aren't used to train our models. ChatGPT can make mistakes. ?
|@ @& ChatGPT4o S Q)
winnings > O:
( )
balance += winnings
( )
balance -= bet
balance > O:
play_again = ( )
( )
( )
__hame__ == :
main()
Explanation:
1. Symbols: Symbols are extracted from the dataset's 'job' and 'marital’ columns. Modify this as per
the actual dataset columns available.
2. Reels: The game now features a 3x3 grid of symbols.
3. Paylines: The game checks multiple paylines for winning combinations.
4. Winnings: Winnings are calculated based on matching symbols in any of the paylines.
Run the above code in your local Python environment after inspecting and adjusting the dataset's
columns and data to fit the game logic. This will give you a more complex and engaging slot machine
game.
P T
Mobbin.com workspace chats aren't used to train our models. ChatGPT can make mistakes. ?


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Information architecture
Home Information Architecture (IA) is the discipline dedicated to
organizing information to facilitate easy discovery,
( | ) comprehension, and utilization for task completion. It
‘ ‘ encompasses 4 essential systems: organization, labeling,
L search, and navigation.
Organization systems focus on categorizing and structuring
- - information, labeling systems involve representing
information through text or visual labels, search systems
determine how users locate information, and navigation
[J save & Share systems dictate how users traverse through the system.
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developing a valuable and enjoyable product. UX designers
play a pivotal role in achieving this by effectively organizing
content while balancing the business requirements and the
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|X s ()
Information architecture
Home Information Architecture (IA) is the discipline dedicated to
organizing information to facilitate easy discovery,
( | ) comprehension, and utilization for task completion. It
‘ ‘ encompasses 4 essential systems: organization, labeling,
| search, and navigation.
Organization systems focus on categorizing and structuring
- - information, labeling systems involve representing
information through text or visual labels, search systems
determine how users locate information, and navigation
(J save & Share systems dictate how users traverse through the system.
Establishing a robust foundation in IA is crucial for
developing a valuable and enjoyable product. UX designers
play a pivotal role in achieving this by effectively organizing
content while balancing the business requirements and the
nAanAde Af thA tarant AanidianAn
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|X —@
What is Information Architecture (IA) primarily about?
Designing graphics 1
Home
| Planning marketing strategies 2
‘. , ‘ Developing websites 3
- - Organizing information 4
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Overview

pageType: Marketing Marketing Marketing Marketing Marketing Marketing Marketing Marketing

screenKeywords:
We are actively seeking talented individuals to join
our team. Explore featured roles or view all open roles.
GPU Kernels Engineer  Research San Francisco, CA Apply now >
HW/SW Co-design Engineer  Research San Francisco, CA Apply now >
Model Teacher, Technical (Contract) Research San Francisco, CA Apply now >
Research Engineer  Research San Francisco, CA Apply now >
Research Scientist  Research San Francisco, CA Apply now >
|@ Research Products Safety Company Q
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|Our text models are advanced language processing tools that can generate,
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can read, and DALL-E, a tool for creating vivid images from text descriptions.
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unsolved problems for our mission. Techniques like learning from

human feedback are helping us get closer, and we are actively
researching new techniques to help us fill the gaps.”
Josh Achiam, Researcher at OpenAl
Focus areas
We build our generative models using a technology called deep learning,
which leverages large amounts of data to train an Al system to perform a task.
|Our research on applying Al to audio processing and audio generation has led to
developments in automatic speech recognition and original musical compositions.
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Our current Al research builds upon a wealth of previous projects and advances.
Research - Jun 17,2020 Research - Oct 15, 2019 ’ |‘ Research,: Sep 17,2019
|@ Research Products Safety Company Q
We believe our research will eventually lead to artificial
general intelligence, a system that can solve human-level
problems. Building safe and beneficial AGl is our mission.
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Pricing (API)

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pagePatterns:
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pagePatterns:
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screenKeywords:
@ Research Products Safety Company Q
Pricing Show prices per 1K tokens
Simple and flexible. Only pay for what you use.

Language models

Multiple models, each with different capabilities and price points. Prices can be viewed in
units of either per 1M or 1K tokens. You can think of tokens as pieces of words, where 1,000

tokens is about 750 words. This paragraph is 35 tokens.

Language models are also available in the Batch API that returns completions within 24
hours for a 50% discount.

Learn about the Batch API 2
|Pricing Show prices per 1K tokens

GPT-4 Turbo
With 128k context, fresher knowledge and the broadest set of capabilities, GPT-4 Turbo is
more powerful than GPT-4 and offered at a lower price.
Learn about GPT-4 Turbo 2

Model Input Output

gpt-4-turbo-2024-04-09 $10.00 / 1M tokens $30.00 / 1M tokens

Vision pricing calculator
Set width Set height

150 px by 150 px = $0.00255 i

Low resolution

With broad general knowledge and domain expertise, GPT-4 can follow complex
instructions in natural language and solve difficult problems with accuracy.
Learn about GPT-4 >
|@ Research Products Safety Company Q
ChatGPT API Explore more

For Everyone Platform overview OpenAl for business

For Teams Pricing Stories

For Enterprises Documentation 2

Pricing APllogin 7

ChatGPT login 2
|Pricing ) Show prices per 1K tokens
What'’s a token? —
You can think of tokens as pieces of words used for natural language processing. For
English text, 1token is approximately 4 characters or 0.75 words. As a point of reference,
the collected works of Shakespeare are about 900,000 words or 1.2M tokens.

To learn more about how tokens work and estimate your usage...

* Experiment with our interactive Tokenizer tool.

¢ Log into your account and enter text into the Playground. The counter in the footer
will display how many tokens are in your text.

Which model should | use? +

How will | know how many tokens I've used each month? +

How can | manage my spending? +

Is the ChatGPT API included in the ChatGPT Plus subscription? +

Does Playground usage count against my quota? +

How is pricing calculated for Completions? +

How is pricing calculated for Fine-tuning? +

Is there an SLA on the various models? +

Is the API available on Microsoft Azure? +
|Pricing Show prices per 1K tokens
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Pay as you go

To keep things simple and flexible, pay only for the resources you use.

Choose your model

Use the right model for the job. We offer a spectrum of capabilities and price points.
What'’s a token? —
You can think of tokens as pieces of words used for natural language processing. For
English text, 1token is approximately 4 characters or 0.75 words. As a point of reference,
the collected works of Shakespeare are about 900,000 words or 1.2M tokens.
To learn more about how tokens work and estimate your usage...
* Experiment with our interactive Tokenizer tool.
¢ Log into your account and enter text into the Playground. The counter in the footer

will display how many tokens are in your text.
|Pricing Show prices per 1K tokens
With broad general knowledge and domain expertise, GPT-4 can follow complex
instructions in natural language and solve difficult problems with accuracy.

Learn about GPT-4 >

Model Input Output

gpt-4 $30.00 / 1M tokens $60.00 / 1M tokens

gpt-4-32k $60.00 / 1M tokens $120.00 / 1M tokens
GPT-3.5 Turbo
GPT-3.5 Turbo models are capable and cost-effective.

gpt-3.5-turbo-0125 isthe flagship model of this family, supports a 16K context
window and is optimized for dialog.

gpt-3.5-turbo-instruct isan Instruct model and only supports a 4K context
window.
Learn about GPT-3.5 Turbo 7

Model Input Output

gpt-3.5-turbo-0125 $0.50 / 1M tokens $1.50 / 1M tokens


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