“Context rot” is an emerging term for the decline in an artificial intelligence (AI) model’s performance as the amount of information it must process grows, even when that information fits within its advertised context window.
An AI model’s context window is the amount of text and other information it can consider when generating a response, including instructions, documents and previous messages.
A larger context window allows users to feed models lengthy reports, entire codebases or extended conversations, but it does not guarantee that every detail will receive equal attention or be used accurately.
As the context grows, a model may overlook important instructions, confuse details, lose track of earlier decisions or produce answers that contradict information supplied previously.
The problem matters as companies increasingly use AI assistants to analyse large document collections, write software and manage complex, multi-step tasks that depend on information gathered over time.
Context rot is not the same as an AI hallucination, in which a model generates false or fabricated information, although the two problems can overlap when missing or misinterpreted context leads to an incorrect answer.
Nor does it necessarily mean a model has reached its technical context limit, because performance can deteriorate before that limit is reached.
Developers can reduce the risk by retrieving relevant information when needed, removing redundant material, summarising lengthy conversations and testing models on realistic tasks rather than relying solely on advertised context-window sizes.