Kapa.ai reduces RAG hallucinations by stripping unnecessary context

Kapa.ai developed a technique to identify and remove irrelevant information from RAG (retrieval-augmented generation) context before sending it to an LLM, cutting down noise that causes hallucinations and wasted tokens. For indie builders using RAG in production, this means cheaper API calls and more reliable answers—especially useful if you're fighting token limits or accuracy issues in your own AI product.