> unless the LLM companies manage to make another big leap.
Why is it a big leap? If the behavior you want can already be elicited by models just with the right level prompting, it's something that can be trained toward. As a simple mental model you could for instance imagine training a verifier-type model of Claude that given a problem spits out a prompt detailing "its success criteria, never to delete tests, never to relax requirements X & Y & Z, always to use this exact feedback loop when testing after it iterated on a fix, and a bunch of others." Also things like specific feedback loop or agentic harnesses will also end up being trained in, similar to how Claude is specifically trained for use with Claude Code.
Thinking of "prompt engineering" as a skill is a fools game, these are language models after all. Do you really think you will hold an advantage over them in your ability to phrase things?
> If the behavior you want can already be elicited by models just with the right level prompting
As mentioned upthread, it's not an all-or-nothing. "Just the right prompting" did get me farther than a few other people but I am very sure it's only a temporary advantage as you yourself alluded to (in a rather emotionally loaded way for reasons unknown).
LLMs can't do everything; they need a good feedback loop where they can ascertain if what they did works. There is a LOT of work out there that is not that (f.ex. game development, firmware / embedded work).
The LLM companies are already putting better feedback loops and agentic harnesses; even the upgrade from Opus 4.5 to 4.6 clearly demonstrated that to me. They don't want their GPUs to burn because people can't be bothered to think of an obvious edge case so they'll make the models smarter to compensate for deficiencies in the human operators.
> Do you really think you will hold an advantage over them in your ability to phrase things?
Regardless of your seeming snark the answer is yes, I do. But as said above, that's not going to last long. Who cares though, I make money in the meantime.
Why is it a big leap? If the behavior you want can already be elicited by models just with the right level prompting, it's something that can be trained toward. As a simple mental model you could for instance imagine training a verifier-type model of Claude that given a problem spits out a prompt detailing "its success criteria, never to delete tests, never to relax requirements X & Y & Z, always to use this exact feedback loop when testing after it iterated on a fix, and a bunch of others." Also things like specific feedback loop or agentic harnesses will also end up being trained in, similar to how Claude is specifically trained for use with Claude Code.
Thinking of "prompt engineering" as a skill is a fools game, these are language models after all. Do you really think you will hold an advantage over them in your ability to phrase things?