Kapa.ai cuts RAG context bloat with selective pruning

Kapa.ai built a system that strips unnecessary context from RAG prompts before sending them to LLMs, reducing token consumption and latency. For makers building AI features on tight budgets, this approach directly lowers inference costs and speeds up response times without sacrificing answer quality.