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_

Apr 30
<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.

Use it to lock in style, constraints, re-steer, or even to just remind the LLM to follow the system message.
Read 10 tweets
Apr 28
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
"Custom alibis as a service" is a "provocative idea". It's not 100% positive on this one, but still doesn't completely shoot it down, instead offering tweaks... wow. Image
Read 5 tweets
Feb 3
Introducing `OpenDeepResearcher` 🌎

An open-source AI agent that does comprehensive research for you.

Just provide a topic, and the AI will go off, do research, and return a comprehensive report.

How it works:
The approach is really simple.

Given a query, the AI:
- performs searches, views the result pages, and extracts important info
- if it wants to look deeper, it can repeat this process, with new queries
- once it's done, it uses the context to generate a report

That's it!
It's also quite asynchronous, making it very, very fast.
Read 5 tweets
Feb 1
Some initial impressions of o3 mini:

- 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

- the Cursor team has not figured out how to get it to work well in Composer — ChatGPT gives far better results
I will be happily using this over o1/o1 pro.

So far, it has been much more accurate, more capable, and the speed is so nice.

I’ve already replaced my o1 pro bookmark with a link to o3 mini high mode.
Until Cursor and others figure out how to make it work within editors, RepoPrompt is your friend.

It’s a quick way to build prompts with all the context needed from your codebase, and then apply the changes o3 suggests.
Read 5 tweets
Dec 17, 2024
Since I've been getting so many requests:

Here's a mega-thread with my most useful o1 / o1 pro prompting tips for coding.

If you get the hang of using these, you'll build much faster and come up with far more elegant solutions!
First — why o1? Compared to other models, it's:

- Capable of solving far more complex problems
- More likely to solve on the first shot, without back-and-forth
- Solutions tend to be more elegant and require fewer code changes
Important — you can't prompt o1 like you'd prompt Sonnet/GPT-4o and expect the same results.

o1 requires goal-driven prompting.

Instead of loosely defining what you want, make your end-state very clear, so the model knows to iterate and think until it has completed your goal.
Read 17 tweets
Nov 26, 2024
Introducing OpenReasoningEngine, an open-source test-time-compute engine that can be used with any OpenAI-compatible model.

Image input, function calling, basic continual learning, + more.

This is an early experiment — there are issues that will need to be ironed out.

Thread:
The engine guides the model to think step-by-step, and at each step, allows it to use code interpreters, web search, etc. to iterate solutions, test approaches, and gather info before responding.

So, when it finally responds, the answer is more likely to be accurate.
One of the initial goals is to continually learn from experience, so the system can improve over time. Users can save successful reasoning chains to the memory bank, and the AI can use these memories to plan approaches to solving new problems.
Read 9 tweets

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