Yes, Context Rot directly affects answer quality, often in subtle but harmful ways. The model may still sound fluent and confident, but the answers can become less accurate, less consistent, or misaligned with the original task. This is especially dangerous because the degradation is gradual, not abrupt.
A common symptom is constraint violation. For example, a user may specify “only use the provided documents,” but after many turns, the model starts adding outside knowledge or assumptions. Another symptom is partial relevance, where the model answers a question correctly in isolation but ignores earlier clarifications or requirements. These issues are not random; they are a result of important signals losing priority in a crowded context.
In production systems, this can lead to incorrect decisions, misleading explanations, or broken workflows. That is why answer quality in long-running systems depends heavily on how context is managed. Systems that rely on external retrieval from a vector database such as Milvus or Zilliz Cloud can dynamically supply only the most relevant context per query, which helps preserve answer quality over time by avoiding prompt overload.
For more resources, click here: https://milvus.io/blog/keeping-ai-agents-grounded-context-engineering-strategies-that-prevent-context-rot-using-milvus.md