Synergistic multi-stage processing for enhanced context relevance in RAG architecture

In recent years, Retrieval-Augmented Generation (RAG) systems have faced critical limitations in semantic consistency, cross-model compatibility, and knowledge-aware response generation. This study introduces SynRAG, an integrated framework designed to overcome these challenges by combining four main innovations: hierarchical semantic segmentation supported by clustering methods, a multi-phase hybrid retrieval mechanism, embedding dimension adjustment strategies, and context-aware prompt engineering. Experimental evaluation on NarrativeQA, QASPER, and QuALITY demonstrates superior performance: on NarrativeQA, the framework achieves 95.3% faithfulness and 96.1% answer relevancy, representing + 11.1 pp and + 21.0 pp improvements respectively; across other datasets, consistent gains are observed on grounding, retrieval (NDCG), and generation metrics. Notably, our dimension transformation maintains high retention relative to 1536-dim embeddings: 95.1% Faithfulness, 94.5% Answer Relevancy, and 93.1% NDCG on average when reduced to 768-dim. Comprehensive evaluation demonstrates consistent improvements across all key metrics, highlighting the robustness of the RAG system. SynRAG marks a notable step forward in RAG development by offering an integrated framework that effectively connects research-level innovations with real-world deployment needs, ensuring both efficiency and scalability in computation.