context-engineering

To structure the AI context window for retrieval-augmented generation, you allocate space for system prompts, retrieved documents, history, user input, and working space based on task requirements to optimize output quality.

Context budgeting in prompt architecture is the deliberate allocation of space across system prompts, retrieved context, conversation history, user input, and working space to maximize model attention and minimize redundancy.

Design information architecture for multi-step reasoning by defining the order and grouping of context blocks, ensuring deliberate budgeting of system prompts and retrieved documents to maximize model attention.

Context engineering works for long-running conversations with memory constraints by applying structured context budgeting and selection criteria to manage conversation history and working space effectively.

The best way to monitor context selection criteria for quality output is to define retrieval strategies and quality monitoring parameters that evaluate how well the context composition satisfies task requirements.