The article presents a Retrieval-Augmented Generation (RAG) approach for automated anomaly detection in manufacturing batch records. By retrieving relevant historical data and generating insights, the system flags deviations without requiring operators to read each record manually. This reduces manual workload and improves timely defect identification in production lines.