Comparison guide
SuperCompress vs Self-RAG
Self-RAG lets the model decide when to retrieve and how to reflect on retrieved passages. SuperCompress takes a different approach: compress all context against the question before generation. Both improve quality but through different mechanisms.
Self-RAG approach
Self-RAG trains the LLM to generate special tokens that trigger retrieval, reflection, and critique. When the model needs more information, it retrieves; when it has enough, it generates. This is powerful but requires fine-tuning or prompt engineering specific to each model.
The cost: Self-RAG adds complexity and can increase latency when multiple retrieval-reflection cycles are triggered.
SuperCompress approach
SuperCompress works with any model, no fine-tuning needed. It is a pre-processing step that removes irrelevant context before the model sees it. This means:
- No model changes — works with GPT-4o, Claude, Llama, any model
- Deterministic compression — same query, same context = same compressed output
- ~60ms latency — much faster than extra retrieval-reflection cycles
Frequently asked questions
Can I use both together?
Yes. Use Self-RAG's on-demand retrieval with SuperCompress as a pre-generation compressor for any retrieved content.
Which is easier to implement?
SuperCompress. Three lines of code vs custom fine-tuning or complex prompt chains.
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.