Prompt compression guide
Prompt compression that keeps the answer
Prompt compression is the practical way to reduce oversized LLM requests before they hit an expensive model. Also called smart context compression when the same query-aware policy runs on retrieved dumps, agent memory, or RAG chunks. SuperCompress scores context against the current question and keeps the lines most likely to matter.
How SuperCompress measures compression
Query-aware Neural Keep reads the question and keeps only the original context lines that answer it. Irrelevant log and tool-output lines drop. We score evidence containment, whether required lines survive in their original wording, not a downstream chat-model grade. Methodology: SuperCompress benchmarks.
Why teams pick SuperCompress over Headroom
- True query-awareness, scores blocks against the question; Headroom leans on content-type heuristics.
- Coding-agent MCP first:
npm install -g supercompress-proxy && supercompress setupfor Cursor, Claude Code, Codex; keep your login. No provider API-key mode required. - Agent + API ready, hosted compress API, open-source library, and MCP install for coding agents, not only a local CLI workflow.
- Evidence containment, we measure whether required lines survive in original wording, not only “token % saved.”
Why prompt compression matters
Every LLM call processes your full prompt including context that may be irrelevant to the question. In agent loops, RAG pipelines, and coding assistants, context accumulates rapidly.
Without prompt compression, you pay for every token regardless of relevance. A typical RAG query sending 4,000 tokens may only need 400 of them to answer the question.
How prompt compression works
SuperCompress uses a learned query-aware neural policy that scores every block of context against your question, then keeps only the blocks most likely to contain answer-relevant information.
from supercompress import Compressor
compressor = Compressor()
result = compressor.compress(
context=long_context,
query="What caused the failed deployment?"
)
print(f"Removed {result.tokens_removed} tokens")Prompt compression vs summarization
| Factor | Prompt Compression | Summarization |
|---|---|---|
| Method | Selects original lines | Rewrites with LLM |
| Extra cost | CPU preprocess (no extra LLM) | Full LLM call |
| Query-aware | Yes, scores lines against the question | Rewrite is not line selection |
Cost savings from prompt compression
Savings depend on how much bulky context you send per call. Measure original_tokens and kept_tokens on your own traffic and compare your provider bill per finished task.
Frequently asked questions
Is prompt compression the same as summarization?
No. Summarization rewrites the prompt using another LLM call, changing wording and potentially losing facts. Query-aware compression selects original lines that matter for the current question.
How much can prompt compression save?
Savings depend on how noisy the dump is. Measure original_tokens / kept_tokens (or tokens_saved) on your traffic. Published suites live on /benchmarks.
Does prompt compression rewrite the prompt?
No. SuperCompress is extractive and query-aware: it keeps original lines chosen against the question and never rewrites or summarizes. Compression is a preprocess before the LLM call.