context-fundamentals

Context engineering structures system prompts, tool definitions, and message history to optimize AI agent performance. It ensures models receive relevant information efficiently, improving task accuracy and managing limited context windows effectively.

Attention mechanisms within fixed context windows limit how much information an LLM processes simultaneously. Understanding these constraints helps manage message history and tool outputs, ensuring critical details aren't overlooked during complex agent tasks.

Progressive disclosure is a context management technique that loads information incrementally rather than all at once. This approach optimizes the context budget by retrieving documents and tool outputs only when needed for specific agent actions.

Structure system prompts by defining core instructions and tool definitions clearly, then manage memory through message history and tool outputs. This foundational context engineering ensures the assistant performs tasks accurately and efficiently.

Effective context budgeting balances system prompts, retrieved documents, and message history within attention constraints. Hybrid strategies combining file-system-based access with progressive disclosure help manage limited context windows efficiently.

Use file-system-based access when managing large volumes of retrieved documents or tool outputs that exceed context window limits. This hybrid strategy supports efficient context loading by retrieving data on demand.