context-fundamentals

Context engineering manages the system prompts, tool definitions, message history, and retrieved documents available to language models. Proper context engineering directly improves AI agent reliability, efficiency, and safety by preventing information overload.

You design context-efficient prompts by applying progressive disclosure techniques to manage the attention budget. This involves structuring workflows and retrieval strategies so models receive only the most relevant context for the immediate task.

An attention budget in prompt design represents the limited computational focus a language model applies across its available context. Managing this budget ensures critical information is prioritized over irrelevant message history or excessive tool outputs.

You debug context issues and reduce token costs by validating context quality and eliminating unnecessary message history. This skill provides foundational concepts to audit retrieval strategies and optimize the context skeleton for specific tasks.

Use progressive disclosure in agent workflows when managing complex tasks with large tool definitions or retrieved documents. It prevents attention budget exhaustion by revealing context layer-by-layer, keeping token usage efficient and agent responses reliable.