Context Compression: 7 Techniques That Cut Tokens Without Losing Signal
Context compression cuts token costs 60-80%. Seven techniques for compressing LLM context without losing the signal that drives quality.
The most expensive tokens in AI are the ones the model does not need. Context compression is the practice of shrinking what you send — history, files, instructions — while keeping the signal that drives quality. Done well it cuts token spend 60-80% on the same work, which is the fastest cost fix that does not touch model choice.
Menlo Ventures projects LLM inference spend reaching $15B by the end of 2026, with 40-60% of enterprise spend wasted. Compression attacks the waste side directly. These seven techniques are ordered from easiest to most advanced — implement them in order. LayerFlow's smart compress automates the workflow; pricing covers the details.
1. Lead with the task
Put the instruction first, context after. Models weight the beginning of the window heavily, and a buried instruction forces repetition — which is itself token waste. Task first, then context, then constraints, then output format.
2. Diff instead of full files
Never paste a 500-line file when the change is ten lines. Send the diff, the interface, or the relevant function signature. A developer debugging an error needs the 20 lines around the stack trace, not the whole repository.
3. Preserve decisions, drop reasoning
When compressing a long session, keep the conclusions and constraints; discard the intermediate reasoning. The next session needs to know what was decided and what is off-limits — not how the model got there. This is the single biggest win when porting history.
4. Summarize hierarchically
For genuinely long context, summarize in layers: a paragraph per major section, then a sentence per paragraph. Two-level compression preserves structure while cutting volume by an order of magnitude. The summary should read as state, not narrative.
5. Strip formatting and fluff
Markdown tables, repeated boilerplate, and conversational filler cost real tokens. Convert tables to compact key-value lists, strip greetings and pleasantries, and remove anything that does not change what the model should do.
6. Set max output tokens honestly
If the answer needs 200 tokens, request 200, not 4,000. Output tokens are the expensive ones on every major provider, and unused headroom still gets charged. Right-sizing output is free money.
7. Cache what repeats
For repeated tasks with stable instructions, prompt caching lets you pay a fraction of the input cost on re-runs. Combined with compression, caching turns the most repetitive 20% of your workload into the cheapest 20%.
What never to compress
- Constraints and exclusions — they define correctness.
- Security rules and compliance requirements.
- The current error message or stack trace, verbatim.
- Exact names: files, functions, services — paraphrasing breaks references.
Internal next steps
Pair compression with Context Window Budgeting and Token Cost Optimization. For the engineering layer, read LLM Context Compression.
Compress your next session: sign in to LayerFlow and run a chat through smart compress, or start with pricing.
FAQ
How do I compress LLM context?+
Lead with the task, send diffs instead of full files, preserve decisions while dropping reasoning, summarize hierarchically, strip formatting, right-size output tokens, and cache repeating instructions.
Does context compression hurt output quality?+
It usually improves it. Noise dilutes attention; compressed context focuses the model on the signal. The exceptions are constraints, security rules, and exact identifiers — never compress those.
How much can context compression save?+
Teams typically cut token spend 60-80% on the same workload by compressing history and trimming context, without touching model choice — the fastest cost fix available.
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