RAG guide
RAG compression evaluation
Adding compression to a RAG pipeline changes the context the LLM sees. Proper evaluation ensures that quality is maintained while costs are reduced.
Evaluation framework
- Answer accuracy — Does the LLM still answer correctly with compressed context?
- Faithfulness — Does the answer hallucinate or contradict the compressed context?
- Relevance — Is the compressed context sufficient for the answer?
- Compression efficiency — What percentage of tokens was removed?
Frequently asked questions
What evaluation dataset should I use?
Use 100-500 real queries from your production logs with human-verified ground truth answers.
How much quality regression is acceptable?
Zero regression is ideal. If you see regression, reduce the compression budget.
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.