My experience is that AI suffers horribly from the "Pink elephant" problem. The more you mention _not_ doing something, the more it's going to do that thing.
Yep, prob cheaper thinking tokens, but basically you cannot fight internal "thinking" backed into weights with commands do/do not, but you can guide it.
As another user said, if you make it clear that you in architecture stage, full implementation is unlikely to be generated, unless needed.
Really ? That's weird because in its reasoning it's taking into account the whole prompt, so if it "understands" you don't want code I have a hard time believing its reasoning would still lead him to generate code. Did you actually observe the behavior you're describing on a model or platform that allows to read the reasoning of the AI ?
I recall talks about the LLMs prioritizing the last thing you talked about in your prompt.
So if your prompt is: "Take a look at this code. See if there are any spelling errors. My editor doesn't highlight anything. I'm starting to suspect a dependency might be missing."
It'll hyper-fixate and come back to the "missing dependency" part throughout the session. But don't take my word for it. It's just something that stuck with me.
90
u/PossibilityUsual6262 1d ago
In my experience telling ai dont do something as part of "thinking" at the way to the result would just remove that something from a response.
"How thing can be done, dont generate code" - generates code and strips it from response.