Matt Shumer Profile picture
Sep 12, 2023 8 tweets 3 min read Read on X
Here's a simple guide to set up your OpenAI Playground for day-to-day use, as a (better!) replacement for ChatGPT.

I've been getting so many questions about this, so hopefully this is helpful!

Read on:

Image
First, why would you want to use the Playground over ChatGPT?

- Greater system prompt/behavior control
- Save multiple system prompts
- Temperature/creativity control
- Longer outputs for reasoning prompts/working with longer text
- Non-nerfed models :)
- Edit all messages

Etc.
So how can you set it up in a way that makes it as frictionless as using ChatGPT?

We'll do this by creating a 'preset' that enables instant access to an optimized setup.

Let's get started:
First, go to .

Make sure you're in Chat mode and select GPT-4, or GPT-4-32K, if you have it! platform.openai.com/playground/
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Next, set temperature to 0.4. I find this is a good starting point for most use-cases, and you can adjust from there for each use-case.

Increase maximum length to enable longer outputs than ChatGPT offers.

For GPT-4, do 3000. For GPT-4-32K, set it to 8000. Image
After that, paste your system prompt here.

Here is a useful system prompt if you don't yet have one:
Image
Finally, press 'Save' and save the preset with a name you'll remember.

Then you can take the link of the page and bookmark it to access this preset whenever you need help from AI! Image
Using this setup will 10x what you're able to do with OpenAI models.

I hope you find this helpful!

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More from @mattshumer_

Jul 30, 2025
Introducing `AutoRL` 📈

The world's simplest way to train a task-specific LLM with RL.

*Just write a SENTENCE describing the model you want.*

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Powered by ART, it's open source.

Link in thread:
How AutoRL works, in a nutshell:

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Ex: "A model that detects spelling and grammar errors"

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Under the hood, this is powered by @OpenPipeAI's ART library.

Their RULER technique made this possible, as it doesn't require labeled output data.

With no need for labeled outputs, we can just sample a LLM to generate scenarios to RL against. Absolutely crazy!
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<developer_note> is one of the most powerful, yet underutilized prompting techniques.

How (and why) to use it:
Oftentimes, just using a system prompt isn't enough to steer a LLM to the degree you want.

Maybe your instructions are complex or super detailed.

Or maybe the LLM's behavior starts to drift once the context window grows.

In these cases, <developer_note> can help you.
What is <developer_note>?

Think of it as your private “note” to the model— invisible to the end-user, but useful to subtly steer the model in the way you want.

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Asking GPT-4o to judge increasingly terrible business ideas until it finally tells me one is bad…

First up, "Soggy Cereal Café": Image
Apparently "WhiffBox" has "serious potential". Image
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Introducing `OpenDeepResearcher` 🌎

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That's it!
It's also quite asynchronous, making it very, very fast.
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- it’s clear that the benchmarks don’t fully capture how good this model is — it’s clearly the best model I’ve used, for code

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I’ve already replaced my o1 pro bookmark with a link to o3 mini high mode.
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