That's a brilliant and witty way to put it :D

Hmm... Where is the new language in this? The specs is just human language and some JSON for defining structures. It's more so that the human language becoming a programming language with the help of AI.

You’re right, it is just English language.

And over time, people will discover some basic phrases and keywords they can use to get certain results from the AI, and find out what sentence structures work best for getting a desired outcome. These will become standard practices and get passed along to other prompt engineers. And eventually they will give the standard a name, so they don’t have to explain it every time. Maybe even version schemes, so humans can collaborate amongst themselves more effectively. And then some entrepreneurs will sell you courses and boot camps on how to speak to AI effectively. And jobs will start asking for applicants to have knowledge of this skill set, with years of experience.

>And over time, people will discover some basic phrases and keywords they can use to get certain results from the AI, and find out what sentence structures work best for getting a desired outcome.

This just sounds like a language that is hard to learn, undocumented, and hard to debug

Yes, but think of the savings!

Until one day a new LLM gets released, GPT5, that doesn't recognize any of those special words. Mastering prompt-speak is essentially mastering undefined behaviors of C compilers.

What you could do is just continue speaking to GPT4 and have it output prompts compatible with GPT5. Pretty simple.

gpt4 won't know anything about gpt5, you would have to make a sophisticated prompt for gpt4 that converts its quirks into gpt5's quirks, but if you know so much about both LLMs, why not to use gpt5 directly?

The idea is someone would first make a prompt for GPT4 that outputs GPT5 enabled prompts. You would initialize GPT4 with it, and then speak to GPT4 to compile prompts to GPT5 context which then gets fed to GPT5.

Although you may know about LLMs, you might specialize in speaking to specific models and know how to get optimal results based on their nuances.

prompts results aren't static and will definitely not be guaranteed over time...

Yes, as always the essential complexity of software is understanding and describing the problem you are trying to solve. Once that is done well, the code falls out fairly easily.

That’s like saying a painting, once they understand what they are trying to paint, just falls out of a painter. It is true but not useful for not-painters.

I think the difference here is that code is effectively a description, so there is an extremely tight coupling between describing the task and the task itself.

You could tell me, in the most painstaking detail, what you want me to paint, and I still couldn't paint it. You can take any random person on the street and tell them exactly what to type and they'd be able to "program".

That's just picking nits with the metaphor. Change it to a poet or a novelist and it works the same. If you tell a person exactly what to write they are just a fancy typewriter, not a poet or novelist. Same with code.