My comment was a summary of the situation, not literal prompts I use. I absolutely realize the work needs to be adequately described and agents must be steered in the right direction. The results also vary greatly depending on the task and the model, so devs see different rates of success.

On non-trivial tasks (like adding a new index type to a db engine, not oneshotting a landing page) I find that the time and effort required to guide an LLM and review its work can exceed the effort of implementing the code myself. Figuring out exactly what to do and how to do it is the hard part of the task. I don't find LLMs helpful in that phase - their assessments and plans are shallow and naive. They can create todo lists that seemingly check off every box, but miss the forest for the trees (and it's an extra work for me to spot these problems).

Sometimes the obvious algorithm isn't the right one, or it turns out that the requirements were wrong. When I implement it myself, I have all the details in my head, so I can discover dead-ends and immediately backtrack. But when LLM is doing the implementation, it takes much more time to spot problems in the mountains of code, and even more effort to tell when it's a genuinely a wrong approach or merely poor execution.

If I feed it what I know before solving the problem myself, I just won't know all the gotchas yet myself. I can research the problem and think about it really hard in detail to give bulletproof guidance, but that's just programming without the typing.

And that's when the models actually behave sensibly. A lot of the time they go off the rails and I feel like a babysitter instructing them "no, don't eat the crayons!", and it's my skill issue for not knowing I must have "NO eating crayons" in AGENTS.md.