RAG guide
Multi-hop RAG compression
Multi-hop RAG answers questions that require multiple retrieval and reasoning steps. Each hop adds more context and more tokens. Compression at each hop keeps the total cost from exploding.
Multi-hop costs
A 3-hop query might: (1) retrieve the user's account info, (2) retrieve the support ticket history, (3) retrieve the relevant knowledge base article. Without compression, each hop adds 2,000-5,000 tokens. With compression, each hop removes irrelevant content from previous hops.
Frequently asked questions
Should I compress at every hop?
Yes. Compress before each LLM call to prevent context accumulation.
Does compression affect multi-hop reasoning?
No. Only irrelevant content is removed at each hop. Reasoning chains are preserved.
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.