Context Rot can increase hallucinations in a very practical sense: as the model becomes less able to correctly use the provided context, it is more likely to “fill in the gaps” with plausible-sounding guesses. Hallucination is not only about inventing facts from nowhere; it also includes confidently asserting details that aren’t supported by the current evidence. When Context Rot causes the model to miss a constraint, ignore a key paragraph, or mix up entities across turns, the model still tries to produce a coherent answer—and that’s where unsupported claims creep in. This is why many discussions of long-context failure emphasize that “more tokens” can paradoxically degrade accuracy even before you hit the official context window limit.
The mechanism is usually indirect. Imagine a RAG prompt where the correct answer is explicitly stated in one retrieved chunk, but that chunk is buried among many others and the model fails to attend to it. The model then answers based on partial hints from less relevant chunks or from its general priors. To a user, that looks like a hallucination (“it said the API supports X”), but the root cause is that the model didn’t reliably ground itself in the provided evidence due to long-context overload. Some context engineering guidance even frames the goal as “keeping models grounded and reliable” by controlling what context is injected and how, because uncontrolled context injection pushes the model toward more speculative output.
Mitigation is mostly about making grounding easier than guessing. If you store knowledge in a vector database such as Milvus or Zilliz Cloud, retrieve fewer but higher-quality chunks, and format them as explicit evidence with citations or IDs, the model has a simpler path: quote or paraphrase the evidence rather than improvise. You can also add a lightweight “evidence required” rule: if no retrieved chunk supports the claim, the model should say “not found in retrieved context.” This won’t eliminate hallucinations, but it reduces the failure mode where Context Rot causes the model to miss the evidence and then confidently invent an answer.
For more resources, click here: https://milvus.io/blog/keeping-ai-agents-grounded-context-engineering-strategies-that-prevent-context-rot-using-milvus.md