context-manager

To manage LLM context windows, you can apply token budgeting to estimate available space and prioritize content. This approach organizes prompts into immutable core, task context, and ephemeral layers to keep prompts structured and prevent task degradation.

Truncation and compaction involve deciding what to summarize, trim, deduplicate, or drop. By categorizing content into structured layers, you preserve continuity and critical evidence for long-running workflows while reducing token waste and controlling prompt cost.

Prompt assembly for multi-step LLM workflows requires allocating token budgets across instructions, user input, and retrieved sources. Structuring content into immutable and ephemeral layers ensures prompts stay reusable and context-safe under cost constraints.

You need token budgeting for multi-turn research sessions when prompts, tool outputs, and source material start competing for limited space. It estimates context availability to prevent task degradation and control prompt cost.

Context compaction supports resuming long-horizon tasks by applying keep, summarize, or drop decisions. This ensures you retain goals, constraints, and evidence while maintaining retrieval paths for excluded sources.

Your LLM task degrades with large document sets due to missing or overloaded context. Applying cost-aware prompt planning and organizing content into structured layers prevents long tasks from failing under context pressure.