Context Stuffing

Context stuffing is the practice of inserting large volumes of potentially relevant text into the prompt — entire documents, extensive histories, or many retrieved passages — in hopes that the model will find what it needs. It is a blunt alternative to targeted retrieval. While sometimes effective, context stuffing inflates cost, increases latency, and can degrade model focus when the stuffed content is noisy or internally inconsistent.