Yes, prompt chunking can reduce Context Rot when used correctly. Prompt chunking involves breaking large bodies of information into smaller, focused pieces and only including the chunks that are relevant to the current task. This prevents the model from being overwhelmed by unnecessary context and helps preserve attention on what matters most.
However, chunking alone is not sufficient if chunks are blindly appended. If many chunks are included without relevance ranking, the prompt can still become noisy. The benefit of chunking comes from selective inclusion, not just smaller pieces. For example, in a documentation assistant, chunking documents by section and retrieving only the top few relevant sections is far more effective than appending dozens of loosely related chunks.
Chunking works best when paired with retrieval systems. Developers commonly store chunks in a vector database such as Milvus or Zilliz Cloud, retrieve the most relevant chunks for a query, and include only those in the prompt. This approach keeps prompts concise and significantly reduces Context Rot by limiting how much irrelevant information the model must process at once.
For more resources, click here: https://milvus.io/blog/keeping-ai-agents-grounded-context-engineering-strategies-that-prevent-context-rot-using-milvus.md