Context Is All You Need: Efficient Retrieval Augmented Generation for Domain Specific AI

Peixi Xiong, Chaunte W. Lacewell, Sameh Gobriel, Nilesh Jain

Track: Main paper track (up to 5 pages excluding references and appendix)

Keywords: Retrieval Augmented Generation

Abstract:

Effective Retrieval-Augmented Generation (RAG) pipelines face significant challenges when processing domain-specific technical documents containing diverse content types like text, figures, equations, and tables.
We introduce CoRAG, Context-oriented RAG for domain-specific applications, which enhances contextual understanding through a lightweight, two-pipeline architecture: Content Analysis & Enrichment for structured metadata extraction, and Query Processing for context-aware retrieval. Our approach emphasizes preserving structural relationships and semantic connections across different modalities, enabling more precise technical information retrieval.
LLM dataset of complex Telco 3GPP technical specifications, CoRAG achieves 77.00% accuracy while using smaller models than current state-of-the-art methods, establishing a new benchmark for telco-RAG applications.
The system’s efficient design and comprehensive context handling make advanced RAG capabilities more accessible for domain-specific use while maintaining high performance across varying levels of technical complexity.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Submission Number: 1