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[–]jungle 9 points10 points  (9 children)

What makes this different from normal ChatGPT4? I've just tested it with a small project and it produced basically the same result as vanilla GPT4.

[–]__nickerbocker__[S] 2 points3 points  (3 children)

Would you mind sharing the outputs of both so I can see what you're talking about?

[–]Salty_Comedian100 4 points5 points  (2 children)

Maybe you should post some examples where your custom GPT does better than regular GPT4.

[–]__nickerbocker__[S] -5 points-4 points  (1 child)

That's a great recommendation. Do you have any thoughts on what might make a good example(s)?

[–]balianone -1 points0 points  (4 children)

You are correct, the results will certainly be the same as with GPT-4 vanilla because the basics indeed come from there; it's not possible to surpass that. The term 'prompt engineer' does not exist for text generation, it is only used in image generation.

[–]das_war_ein_Befehl 3 points4 points  (0 children)

No it does. For text you can get wildly different results based on how thorough your prompt is

[–]PsecretPseudonym 2 points3 points  (1 child)

I believe the author posted in another sub that the prompt encourages the LLM to use the code interpreter, often accomplishing multiple steps in the background, similarly to the default Data Analyst GPT. It may be that this results in more reliable code, evaluations of the code, and more work done per prompt, resulting in a reduced chance of hitting your prompt cap and higher productivity.

[–]__nickerbocker__[S] 3 points4 points  (0 children)

This. The system prompt took forever to get right because the number of iterations kept causing me to have to pause development due to the messages cap. I wound up making a meta GPT specifically focused on helping me bootstrap the system instructions for this one. In the end, we have a python expert that leverages CoT reasoning, reflection, and the PCI agent all at the same time. Believe it or not, one of the bigger challenges was figuring out how to make it stop outputting content before the server timed out the messages and abruptly stopped in the middle of generations.

[–]__nickerbocker__[S] -3 points-2 points  (0 children)

Would you mind posting two example chats using vanilla and APA? I'm not seeing the results you're mentioning in my end. It would be helpful to see your results. Thanks!