context-management

Manage AI context window limits by implementing tiered token allocation and surgical reading strategies that extract necessary information from large files without loading them entirely. This prevents context exhaustion and reserves space for critical data.

Context budgeting is the process of allocating tokens across tiers like Pinned, Task-Local, On-Demand, Output, and Safety to ensure predictable usage. It prevents token exhaustion by reserving specific context portions for critical tasks and safety protocols.

Read large files without exceeding context limits by utilizing targeted search and surgical reading techniques. This extraction-over-loading approach accesses only necessary information, adapting strategies based on whether files are Small, Medium, Large, or Massive.

When context pressure reaches Yellow, Orange, or Red thresholds, execute context rotation protocols to clear less critical information. This proactive response prevents exhaustion and ensures active tasks retain the memory needed for productive work.

The best way to allocate tokens for long-running AI tasks is implementing a tiered structure, such as reserving 15% of the context window for pinned items and 25% for task-local items, ensuring critical information remains accessible throughout the process.