Ever corrected an LLM five times and still gotten the same kind of answer?
That isn’t stubbornness – it’s context poisoning.
What is context poisoning?
Each time you correct the model, it reprocesses the entire conversation, including its own previous answers. Those earlier AI answers become strong examples of “how to answer your question” – and the model keeps following them.
Why don’t my corrections work?
Your corrections are weak negative signals buried mid-thread. Meanwhile, the AI’s long, detailed responses sit at conversation boundaries (where attention is strongest), so they act like powerful positive exemplars.
When does this problem get worse?
In long analytical workflows. As context grows, the model’s own outputs take up more and more of the conversation. Eventually those outputs outweigh your corrections.
What’s the practical fix?
Use strict structural constraints that override exemplars, or restart fresh with clear requirements from message one.
How can Prism Prompting help?
If you are using a generic LLM you can learn the Prism Prompting Framework in our guide. You can also just throw the article into your LLM project files and prompt the LLM to use the Prism framework – the article’s structure is optimized for this usage.