Context Rot affects Retrieval-Augmented Generation (RAG) systems by reducing the model’s ability to correctly use retrieved documents when too much context is included. In theory, RAG improves accuracy by grounding the model in external data. In practice, if too many retrieved chunks are appended, the model may ignore the most relevant ones or blend them incorrectly with less relevant content.
A common failure mode is retrieval accumulation. Each query retrieves new documents and appends them to the existing context. Over time, the prompt contains many documents from different topics or timeframes. Even if the correct answer is present, it may be buried among other text, causing the model to produce vague or incorrect responses. This looks like a retrieval failure, but it is often a Context Rot problem.
Well-designed RAG systems address this by keeping retrieval stateless or lightly stateful. Instead of accumulating documents, they retrieve fresh context for each query and discard old context. Vector databases such as Milvus or Zilliz Cloud enable this pattern by making it fast to retrieve only the most relevant chunks each time. By controlling context size and relevance, RAG systems can avoid Context Rot and maintain high answer quality over long interactions.
For more resources, click here: https://milvus.io/blog/keeping-ai-agents-grounded-context-engineering-strategies-that-prevent-context-rot-using-milvus.md