通过评估模块提升检索生成系统的准确性与可靠性。

Retrieval-Augmented Generation (RAG) architectures have recently garnered significant attention for their ability to improve truth grounding and coherence in natural language processing tasks. However, the reliability of RAG systems in producing accurate answers diminishes as the volume of data they access increases. Even with smaller datasets, these systems occasionally fail to address simple queries. This issue arises from their dependence on state-of-the-art large language models (LLMs), which can introduce uncertainty into the system's outputs. In this work, I propose a novel Comparative RAG system that introduces an evaluator module to bridge the gap between probabilistic RAG systems and deterministically verifiable responses. The evaluator compares external recommendations with the retrieved document chunks, adding a decision-making layer that enhances the system's reliability. This approach ensures that the chunks retrieved are both semantically relevant and logically consistent with deterministic insights, thereby improving the accuracy and overall efficiency of RAG systems. This framework paves the way for more reliable and scalable question-answering applications in domains requiring high precision and verifiability.