Don't fall into the anti-AI hype

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I very much agree with how you’ve categorized the initial state condition that is amenable to LLM assisted SWE and works well to a greater state of beneficial order. And implicitly I also agree most of the complement to that set of applied contexts yields ~medium to not so productive results.

But what do you mean by “LLM prompting is on average much less analyzable” ? Isn’t structured prompting (what that should optimally look like) the most objective and well defined part of the whole workflow. it’s the lowest entropy part of the situation, we know pretty well what a good LLM prompt is and what will be ineffective, even LLMs “know” that. Do you mean “context engineering” is hard to optimize around ? That’s often thought of interchangeably I think, but regardless that has in fact become the “hard problem” (user facing) in effectively leveraging LLM for dev work. Ever since the reasoning class models were introduced I think, it became more about context engineering in practice than prompting. Nowadays from the very onset Even resuming a session efficiently often requires a non-trivial approach that we’ve already started to design patterns and built tools around, (like CLI coding workflows adding /compact as user directive, etc).

I’m not a software engineer by trade, so I can’t pretend to know what that fully entails at the tail ends of enterprise scale and complexity, but I’ve spent a decent amount of time programming and as far as LLMs go, I think there’s probably somewhere down the road where we get so methodical about context engineering and tooling and memory management, all of the vast still somewhat nebulous surrounding space and scaffolding to LLM workflows that have a big impact on productive use of them—we may eventually engineer that aspect to an extent that will be able to much more consistently yield better results across more applied contexts than the “clean code”/“trivial app” dichotomy. But … I think the depth of additional effort and knowledge and skill required by human user to do this optimal context engineering (once we fully understand how even) to get the best out of LLMs… I think that quickly just converges to — what it means to be a competent software engineer already. the meta layers around just “writing code” that are required to build robust systems and maintain them, the amount of work required to coerce non-deterministic models into effectively internalizing that, or at minimum not fvcking it up… that juice might not be worth the squeeze when it’s essentially what a good developer’s job is already. If that’s true then there will likely remain a ceiling of finite productivity you can expect from LLM assisted development for a long time… (I conjecture).

Yes, you're missing a magic workflow.

If you find yourself having to copy and paste errors back and forward you need to upgrade to a coding agent harness like Claude Code so the LLM can try things out and then fix the errors on its own.

If you're not willing to do that you can also fix this by preparing a text file with a few examples of correctly formatted queries and pasting that in at the start of your session, or putting it in a skill markdown file.