Kapa.ai Reduces RAG Context by 68% While Maintaining 96% Recall
July 6, 2026
A three-step retrieval architecture uses a small, low-cost LLM to prune retrieved chunks before they reach the generator. This method discards 68% of irrelevant context while preserving 96% of answer recall, significantly reducing token costs for complex knowledge bases.
HOW THIS AFFECTS YOU
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builderYou can lower inference costs and latency by implementing a middle-layer pruning step for your RAG pipelines.