A synergistic multi-stage RAG architecture for boosting context relevance in data science literature
Year: 2025
Type: article
Abstract: Navigating the voluminous and rapidly evolving data science literature presents a significant bottleneck for researchers and practitioners. Standard Retrieval-Augmented Generation (RAG) systems often struggle with retrieving precisely relevant context from this dense academic corpus. This paper introduces a synergistic multi-stage RAG architecture specifically tailored to overcome these challenges. Our approach integrates structured document parsing (GROBID), domain-specific embedding fine-tuning derived from textbooks, semantic chunking for coherence, and proposes a novel ’Abstract First’ retrieval strategy that prioritizes concise, high-signal summaries. Through rigorous evaluation using the RAGAS framework and a custom data science query set, we demonstrate that this integrated architecture significantly boosts Context Relevance by over 15-fold compared to baseline RAG, surpassing configurations using only subsets of these enhancements. These findings underscore the critical importance of multi-stage optimization and highlight the surprising efficacy of the abstract-centric retrieval method for specialized academic domains, offering a validated pathway to more effective literature navigation in data science.
Authors: Ahmet Yasin Aytar, Kamer Kaya, Kemal Kılıç
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,740
OpenAlex ID: W7081955927