A new paper presents a method to reduce hallucinations in large language models (LLMs) using a multi-agent pipeline and semantic caching, achieving significant improvements in factual reliability and operational efficiency. The approach demonstrates that memory-augmented designs can enhance performance without the need for model retraining, making it viable for production-scale applications.