It's not higher level programming, because it's imprecise. We don't know how input will impact output, or to what extent, or why. If prompt engineering is programming, so is poetry. Which, I suppose you could make an argument for (calling forth subjective imagery in the reader's mind), but at that point you're sufficiently stretching definitions to the point they mean nothing.
I have been using ChatGPT to test its ability to create JSON on the fly, just with a natural language description of the exact data format, and a natural language description of what I want to go in that object. It even escapes the string properly if it contains special characters, from what I noticed.
Other than complicated requests like “if object is of type A, include fields ABC but not D. If object is of type B, include only D but not other fields”, it gets this right 99% of the time.
It also works for CSV, but it’s trickier. It seems like it “knows” how JSON works to a much better extent.
And as for parsing JSON? I’ve not truly pushed it to its limits yet, but so far it’s had no issues understanding any of it.
It’s mind-boggling. Yes, it’s inefficient, but it can basically parse, generate and process valid JSON with just a brief set of instructions for what you want to do. For exploring ad-hoc data structures or quick mocking of API backends, this is great.
Is “imprecision” that the previous commenter is reacting to maybe more specifically the strong potential for nondeterministic behavior exhibited by LLMs? That would seem to stretch the practical experience of programming vs. “lower-level” tools like Python or C. (Also, a world where I am calling Python “lower-level” is wild).
Prompt generation feels like it's going to reach a point where it becomes more similar to legalese — which in itself feels more similar to a programming language than natural speech.
