How to Properly Allocate Token Budgets Across an LLM Context Window

Managing a large language model's context window requires deliberately dividing a fixed token limit among competing content blocks — system prompts, history, and retrieved documents — before any request is assembled. A key arithmetic mistake is filling the window to capacity without reserving space for the model's output, which causes requests to fail. Each content block should be assigned a floor (minimum useful size), a desired size, and a priority, so that when space is tight, lower-priority blocks are dropped entirely rather than all blocks being uniformly truncated. Fixed elements like system prompts and tool schemas must be allocated first since they cannot be scaled down, while elastic blocks like chat history share whatever space remains. A small safety margin of two to three percent should also be set aside to account for token-count discrepancies introduced by chat templates and role markers during server-side re-serialization.