Improved Naive RAG by Integrated Advanced Techniques: A Comprehensive Framework Using Parent-Child Architecture, Hybrid Retrieval, and Contextual Compression

This paper presents a novel approach to enhance Retrieval-Augmented Generation (RAG) systems through the integration of three advanced techniques: Parent-Child Architecture, Hybrid Retrieval, and Contextual Compression with cross-encoder re-ranking. We implement this framework using Langchain and FAISS vector search, with Anthropic’s Claude as the foundation model. Our comprehensive evaluation across 14 diverse Wikipedia-based knowledge domains employs the RAGAS framework to measure multiple performance dimensions. Results demonstrate that our advanced framework yields significant improvements in key metrics: Context Precision 10.11%, Context Recall 2.25%,and BLEU scores 1.14% compared to Naive RAG implementations. Domain analysis reveals particularly strong performance in Medicine 8.0% BLEU, Science 4.9%, and specialized Technology domains 4.9%. While, some technical domains such as Cybersecurity (−2.4%) and Biology (−6.6%) show performance degradation. Our framework achieves these improvements with minimal computational overhead by 1.89%,offering a practical approach to implementing domain-adaptive RAG systems that optimize context quality for improved generation performance.