RAG guide
Hybrid retrieval with compression
Hybrid retrieval combines the semantic matching of dense embeddings with the keyword precision of sparse retrieval. SuperCompress then selects the most relevant content from both streams.
Why hybrid + compression works
Dense retrieval finds conceptually similar content; sparse retrieval finds exact keyword matches. Together, they return more relevant candidates. The downside: more candidates means more tokens. SuperCompress compresses the combined results against the query, keeping only what matters for the answer.
Frequently asked questions
Does compression negate hybrid retrieval's benefit?
No. Compression selects the best content from both retrieval methods.
What's the optimal K for hybrid retrieval with compression?
Retrieve 15-20 candidates per method (30-40 total), then compress to the best 5-8 chunks.
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.