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.
Try it yourself
Paste your long prompt into the playground, ask a question, and see what SuperCompress keeps and removes. Free, no signup needed.