How to Pack Context for an LLM Without Exceeding the Token Budget
By Rui Barreira · Last updated: 13 June 2026
Every LLM request has a token budget. The input context and output allowance share that budget, so you need to reserve room for the answer before packing documents and history. brevio Context Packer makes that trade-off visible.
Reserve Output First
If a model supports 8,000 tokens and you need a 1,000-token answer, only 7,000 tokens remain for system instructions, policy, retrieved documents, and conversation history.
Prioritize Blocks
High-priority blocks usually include system instructions, safety policy, required schemas, and directly relevant retrieved facts. Older conversation turns and low-confidence retrieval results should be lower priority.
Pack Deterministically
A simple deterministic strategy is to sort blocks by priority and include each one while it fits. This is predictable, easy to debug, and safe for prompt pipelines where you need reproducible behavior.
Production Checks
Approximate token counts are useful for planning. For hard API limits, verify with the provider's official tokenizer before sending requests at scale.
Frequently Asked Questions
- Why reserve output tokens?
- Most APIs count input and output against the same context window. Reserving output space prevents a prompt from leaving no room for the answer.
- What should get highest priority?
- System instructions, safety policy, and task-critical retrieved facts usually rank above older conversation history.
- Is approximate token counting enough?
- It is enough for planning. For production hard limits, verify with the model provider's official tokenizer.
- Does context packing replace RAG?
- No. It helps decide what retrieved or remembered context fits after retrieval has already selected candidate blocks.