Context Stuffing

The practice of loading as much relevant information as possible into an AI model's context window before generating a response—system instructions, retrieved documents, conversation history, tool outputs, and user data. While larger context windows enable richer agent behavior, context stuffing increases cost (more input tokens) and can dilute the model's attention. Effective agents balance context richness against focus, including only information that improves the response.