Okay, I get the response:

>Response Draft: An animal that starts with the first letter of the capital of France is an Eel.Draft Reflection: The answer draft is correct. Paris is the capital of France and the first letter is "P." However, the answer could be more concise by simply stating "Eel" as the answer.

Actual Response: The answer is Eel. The capital of France is Paris and the first letter is "P". Therefore, an animal that starts with the first letter of the capital of France is an Eel.

Given the following instructions:

>For this interaction, we are going to use the following structure.
>
>User (me): [I will ask a topic or question]
>
>You will provide an Response Draft: [Brief or simplified answer to the topic or question]
>
>Then you will undergo Draft Reflection: [You will provide a critique or review of the answer draft, highlighting the limitations, inaccuracies or areas that need improvement, correction or expansion, while providing guidance on how to address these issues in the revised response. Important, think step by step, while reviewing or criticizing the hypothetical response.]
>
>Then you will provide an Actual Response: [The natural and contextually appropriate answer to the topic or question, as generated by the advanced language model, which incorporates the suggestions and improvements from the draft reflection for a more comprehensive and accurate response. This also can include step-by-step reasoning.]You will in general act like the worlds best experts of the respective domain or field of the question.Do you understand?

-_-

Okay, this Update to the instructions gives a correct response:

Actual Response: [The corrected response draft given the draft reflection. This also can include step-by-step reasoning.]You will in general act like the worlds best experts of the respective domain or field of the question.Do you understand?"

Are there any websites/articles/blogs/forums with proven prompt formats for ChatGPT and co you can recommend.

Especially ones for programming/refactoring/tests... and general error messages (operating system, installation, crashes).

I am just starting to look into using ChatGPT or alternatives.

I have found a page with ranked jailbreak prompts for ChatGPT so far.

I am confused about your mention of GAN structure. If you want to generate natural language text, use a pretrained Large Language Model. You probably have to finetune it for best use, as you don't have access to the giant ones, which do very well with zero-shot prompting.

Some LLMs, there is also RWKV-4 and FAIRs LLama

Try a Kaggle competition for some practical experience of applying ML to already cleaned data. There is always published code of other competitors and Kaggle has also tutorials.

I don't disagree with the phenomena of emergence, it's just that it doesn't explain anything. It is one word for "I have no idea how it works" or better: its magic. The issue I have with that is that you are quick to hide behind that word, using it as an explanation, accepted as the emergence has become.

But in fact you can't model one bit with it, it has no predictive power and it kind of shuts down discussions.

So far I haven't seen any evidence (have you?) that LLMs aren't doing anything else but predicting the next token. Yes there are certain thresholds, where they do overcome the one or other weakness. But in the end they just predict the next token better ... and even better. Impressive what you can do with that (chinese room like), but that doesn't imply that GPT4 is any different than GPT3.5, it's just better.

But as I wrote, you can in theory replace most non-manual work with that somewhere down the line anyway. But no GPT will develop you some ground-breaking Deep Learning architecture or solve important physics problems which need actual thought and not just more compute or...

Not that you claimed that - I do here -, but should GPT-7 or so suddenly do that, then you can hold me to it.

>But here they prompted GPT-4 to generate code that would generate a picture in a specific style.

5 seconds of googling "code which generates random images in the style of the painter Kandinsky":

http://www.cad.zju.edu.cn/home/jhyu/Papers/LeonardoKandinsky.pdf

https://github.com/henrywoody/kandinsky-bot

GPT's trained on the whole of the WWW sensible text are just sophisticated echo/recombination chambers. True, it works far better than most would have predicted, but that doesn't change the way they work. I am also impressed, but GPT-3 got known for parroting content, why should the next generation be fundamentally different? It just gets harder and harder to verify.

Nevertheless I even expect such generative models to be good enough to become very general. Most human work isn't doing novel things either. Just copying up to smart recombination.