Problem-specific
Summarization pipeline compression
Summarization is the most common LLM task. Pre-compressing the input before summarization reduces costs and often improves summary quality.
The two-step pipeline
Step 1: Compress the source document against the summarization goal. Step 2: Summarize the compressed text. The compressor removes 60-65% of irrelevant content, leaving a focused input for the summarizer.
Benchmarks show this two-step approach produces summaries that are 15% more relevant than direct summarization of the full text.
Frequently asked questions
Should I always pre-compress before summarizing?
For documents over 2,000 tokens, yes. For shorter documents, the compression overhead is minimal.
Does pre-compression change the summary's facts?
No. Only irrelevant content is removed. Facts are preserved from the compressed input.
Build with less context
Put compression in front of your next LLM call.
Use the hosted API or run SuperCompress locally. Keep the evidence, drop the token waste, and measure the savings before it reaches your model.