I haven’t used Fable/Mythos yet, but my experience with recent version of Opus, GPT 5.5 and recent Chinese models is that promoting again isn’t guaranteed to fix the underlying issues, nor is it guaranteed to not introduce more issues. I’ve seen SOTA models make ridiculously stupid architectural decisions that they were then unable to back out of without being prompted very specifically, instead adding a patchwork of “fixes” on top.
I’m not saying that you can’t use AI to do it because I believe that with carefully controlled workflows and context management you can, but it’s not a simple prompt away, it’s requires guidance and understanding, and isn’t the speed demon that raw prompting is.
> I haven’t used Fable/Mythos yet, but my experience with recent version of Opus, GPT 5.5 and recent Chinese models is that promoting again isn’t guaranteed to fix the underlying issues, nor is it guaranteed to not introduce more issues.
That's not really the point though. That presumes models are only useful if they are one-shot models. That is false.
I mean, what if your prompt successfully changes 20 source files and makes a mess in one? How much work did it saved?
And the elephant in the room is when models actually outperform whatever the prompter is able to deliver, and faster. That is somehow left out.
> That presumes models are only useful if they are one-shot models
That’s not at all what I’m saying.
I’m saying that in my experience across multiple models, the follow up prompts don’t fix prior underlying issues. They usually patch on top instead, unless you give them significant and time consuming guidance.
I want them to be more useful outside of one-shot uses, but I find that they currently miss the mark.