Pruning RAG context down to what the answer actually needs

Kapa.ai developed a cost optimization technique that reduces RAG context by 68% while maintaining 96% recall by inserting a small, lightweight LLM between retrieval and generation to intelligently prune irrelevant chunks before they reach expensive models. This approach cuts query costs by approximately one-third, addressing a critical business challenge where retrieved context represents two-thirds of query expenses, and enables IT organizations to scale AI assistants more economically while maintaining answer quality. For technology leaders, this demonstrates that architectural innovation in AI pipelines can deliver significant operational savings without sacrificing performance—a key consideration for enterprises deploying large-scale knowledge-based AI systems.