Optimizing RAG: Classifying Queries for Dynamic Processing

In Retrieval-Augmented Generation (RAG) systems efficient information retrieval is crucial for enhancing user experience and satisfaction, as response times and computational demands significantly impact performance. RAG can be unnecessarily resource-intensive for frequently asked questions (FAQs) and simple questions. In this paper we introduce an approach in which we categorize user questions into simple queries that do not require RAG processing. Evaluation results show that our proposal reduces latency and improves response efficiency compared to systems relying solely on RAG.