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Context Poisoning

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.

  • Attention bias, Context management, Re-prompting strategy, Structural prompting, Workflow reliability

(0:00) Hi, analysts. (0:02) Today's story is about context poisoning, (0:04) when your corrections accidentally (0:06) make AI responses worse. (0:08) But first, if you want to skip these headaches entirely, (0:12) visit ask-y.ai and try Prism.(0:16) Marcus survived Thanksgiving. (0:18) Now, Friday morning with leftover pie, (0:21) he had one goal, buy a TV. (0:24) He pulled out his phone and asked his floofies.(0:28) Find me the best Black Friday TV deals under $800. (0:34) The Samsung 65QLED at $749 is a good choice, (0:40) featuring 4K resolution, HDR 10 plus support, (0:44) and excellent reviews for vibrant display quality. (0:47) Marcus frowned.(0:48) Too expensive. (0:49) Show me options under $600. (0:54) TCL 554 series at $579.99, offering 4K resolution, (1:00) Roku integration, and solid performance.(1:03) Stop with the specs. (1:04) Just list three TVs with prices. (1:08) Three excellent options.(1:11) TCL 55 inches. (1:13) Hisense 50 inches. (1:14) Samsung 50 inches.(1:17) Reliable quality. (1:18) Premium features. (1:20) Strong performance.(1:20) Marcus groaned. (1:22) Each correction is making the problem worse. (1:25) His context was filling with detailed examples.(1:28) This is what Marcus wasn't realizing. (1:31) Marcus' corrections were weak signals (1:34) buried between strong, structured examples. (1:37) Here is how the floofies' attention mechanism (1:40) locked in on the pattern Marcus didn't want.(1:43) First, we have Marcus' original request. (1:46) Then, first detailed answer. (1:48) That's an example of a clear, complete response.(1:52) Marcus corrects the floofies. (1:54) Second detailed answer. (1:55) The response.(1:56) Pattern is reinforced. (1:58) Marcus gets frustrated. (2:00) Third detailed answer.(2:01) The pattern is locked in. (2:06) This is called context poisoning. (2:10) The strongest, clearest examples in the context (2:13) were the floofies' own previous responses.(2:16) Marcus didn't want to start a new conversation (2:19) to reset his context, so he tried a fix. (2:22) Using extreme constraints. (2:24) Ignore everything above.(2:25) Return only a three-item bullet list. (2:27) Format, brand, size, price. (2:29) No other text.(2:30) No features. (2:31) No descriptions. (2:32) Here are three options for your consideration.(2:35) TCL 5 inches, 4 series. (2:37) Hisense 50 inches, Midas. (2:39) Samsung 5 inches, Midas.(2:42) $599.99. (2:43) Still adding descriptions. (2:48) Marcus tried one more time with even stricter formatting. (2:52) This is called extreme structural constraints.(2:55) Extreme structural constraints work to override (2:58) the poisoned context. (3:00) Output format. (3:01) Brand, size, price.(3:03) Nothing else. (3:04) Match this exactly. (3:08) TCL 55 inch, $579.99. (3:11) Hisense 50 inch, $549.99. (3:13) Samsung 50 inch, $599.99. (3:16) But Marcus realized that fighting poisoned context (3:19) requires exhausting specificity.(3:22) Easier to just start over. (3:24) New chat. (3:25) Clean slate.(3:26) Clean context. (3:27) List three Black Friday TV deals under $600. (3:30) Format, brand, size, price only.(3:32) TCL 55 inch, $579.99. (3:35) Hisense 50 inch, $549.99. (3:37) Samsung 50 inch, $599.99. (3:40) When possible, starting fresh is easier (3:42) than fighting accumulated context. (3:44) When you correct an AI, your corrections (3:46) get added to the context. (3:48) Your corrections provide weaker signal (3:50) than those exemplars unless you use (3:52) extreme structural constraints.(3:56) Provide an exact template to match. (3:58) Override all previous examples with stronger formatting. (4:01) Be extremely specific in your prompt.(4:04) And even so, you are still fighting against (4:06) all the unwanted structured examples in your context. (4:09) So if it fails, start fresh. (4:11) Put clear constraints in your first message.(4:14) Be specific about format. (4:16) Avoid vague negatives. (4:18) Or use Ask-Y's PRISM platform, (4:21) where smart context management prevents your corrections (4:23) from poisoning the conversation.(4:25) Like having a Black Friday assistant (4:27) with a reset button that actually works. (4:29) Visit ask-y.ai and try PRISM.