Kapa.ai cuts RAG costs by pruning irrelevant context before queries

Kapa.ai developed a technique to strip unnecessary documents from RAG (retrieval-augmented generation) context before passing queries to LLMs, reducing token overhead and latency. For indie makers building AI features on tight budgets, this approach could meaningfully lower API costs while improving response speed—especially relevant for documentation Q&A and knowledge-base products.