Having mistakes in context seems to 'contaminate' the results and you keep getting more problems even when you're specifically asking for a fix.
It does make some sense as LLMs are generally known to respond much better to positive examples than negative examples. If an LLM sees the wrong way, it can't help being influenced by it, even if your prompt says very sternly not to do it that way. So you're usually better off re-framing what you want in positive terms.
I actually built an AI coding tool to help enable the workflow of backing up and re-prompting: https://github.com/plandex-ai/plandex