[sara_builds]

The variance mostly comes down to prompt craft and context management.

People who get consistently good results have usually internalized a few things: (1) being explicit about constraints and output format, (2) providing relevant context without noise, (3) matching the model to the task (reasoning-heavy vs creative vs code), and (4) iterating on the prompt when something fails rather than assuming the model is broken.

I've seen the same person get wildly different results depending on whether they ask "write code to do X" vs "I need a function that takes A, returns B, handles edge case C, and should be optimized for D. Here's the existing code it needs to integrate with: [context]."

The gap between those two approaches can be a 10x difference in usefulness. Most of the "LLMs are useless" crowd and the "LLMs are magic" crowd are just working with very different prompt habits.

[tjr]

It appears to me that the people who consistently get the best results from LLM coding tools are prompting fairly close to the code. Maybe not quite at the level of writing pseudocode, but close enough that they really still need to understand software development.

Which seems to not quite gel with the notion of, you don't need programmers, you don't need to know how to program, etc.

I feel pretty confident that, in fact, you don't need to. You probably can get good results without having a clue what you're doing, if you prompt well enough, or prompt long enough, or prompt repeatedly until it works. But I think you will more reliably, maybe even more quickly, get good results if you do know what you're doing, and if you stay reasonably engaged with the development, even if not literally writing the code yourself.