Introducing ChatGPT

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OpenAI introduces ChatGPT, a conversational AI model that interacts with users in a dialogue format

intermediate Published 30 Nov 2022
Action Steps
  1. Try out ChatGPT at chatgpt.com to experience its conversational interface
  2. Explore the model's strengths and weaknesses through user feedback and research
  3. Investigate the use of Reinforcement Learning from Human Feedback (RLHF) for training AI models
Who Needs to Know This

Software engineers, AI researchers, and product managers can benefit from understanding ChatGPT's capabilities and limitations to explore potential applications and integrations

Key Insight

💡 ChatGPT's dialogue format enables more natural and interactive user experiences, with potential applications in customer support, language translation, and more

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🤖 Meet ChatGPT, a conversational AI model that can answer follow-up questions and admit its mistakes! 💡

Key Takeaways

OpenAI introduces ChatGPT, a conversational AI model that interacts with users in a dialogue format

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# Introducing ChatGPT | OpenAI

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Table of contents

* [Methods](https://openai.com/index/chatgpt#methods)
* [Limitations](https://openai.com/index/chatgpt#limitations)
* [Iterative deployment](https://openai.com/index/chatgpt#iterative-deployment)

November 30, 2022

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# Introducing ChatGPT

[Try ChatGPT(opens in a new window)](https://chatgpt.com/)[Try ChatGPT for Work](https://openai.com/business/)

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We’ve trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.

ChatGPT is a sibling model to[InstructGPT⁠](https://openai.com/index/instruction-following/), which is trained to follow an instruction in a prompt and provide a detailed response.

We are excited to introduce ChatGPT to get users’ feedback and learn about its strengths and weaknesses. During the research preview, usage of ChatGPT is free. Try it now at[chatgpt.com⁠(opens in a new window)](https://chatgpt.com/).

### Samples

Fix code Home security Fermat's Little Theorem Neighbor introduction

User

this code is not working like i expect — how do i fix it?
```
resultWorkerErr := make(chan error)
defer close(resultWorkerErr)
go func() {
defer cancel()
resultWorkerErr <- b.resultWorker(ctx)
}()

err := b.worker(ctx)
cancel()
if err == nil {
return <-resultWorkerErr
}
return multierror.Append(err, <-resultWorkerErr)
```

ChatGPT

It’s difficult to say what’s wrong with the code without more context. Can you provide more information about what the code is supposed to do and what isn’t working as expected? Also, is this the entire code or just a part of it?

User

just part of it — the error never surfaces. I think there is something wrong with the channel

ChatGPT

It’s difficult to say without more information about what the code is supposed to do and what’s happening when it’s executed. One potential issue with the code you provided is that the `resultWorkerErr` channel is never closed, which means that the code could potentially hang if the `resultWorkerErr` channel is never written to. This could happen if `b.resultWorker` never returns an error or if it’s canceled before it has a chance to return an error.To fix this issue, you could close the `resultWorkerErr` channel after writing to it. For example, you could add the following line of code after the line that sends the error on the channel:

`close(resultworkerErr)`

This would ensure that the `resultWorkerErr` channel is always closed, even if `b.resultWorker` never returns an error. Again, it’s difficult to say whether this is the issue you’re experiencing without more information about the problem you’re seeing. Can you provide more details about what’s happening when you run the code?

## Methods

We trained this model using Reinforcement Learning from Human Feedback (RLHF), using the same methods as[InstructGPT⁠](https://openai.com/index/instruction-following/), but with slight differences in the data collection setup. We trained an initial model using supervised fine-tuning: human AI trainers provided conversations in which they played both sides—the user and an AI assistant. We gave the trainers access to model-written suggestions to help them co
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