Engineering the RAG Stack: A Comprehensive Review of the Architecture and Trust Frameworks for Retrieval-Augmented Generation Systems

• URL: http://arxiv.org/abs/2601.05264v1
• Date: Fri, 07 Nov 2025 16:26:29 GMT
• Title: Engineering the RAG Stack: A Comprehensive Review of the Architecture and Trust Frameworks for Retrieval-Augmented Generation Systems
• Abstract summary: This article provides a systematic literature review of academic studies, industrial applications, and real-world deployments from 2018 to 2025.<n>It provides a practical framework for the deployment of resilient, secure, and domain-adaptable RAG systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This article provides a comprehensive systematic literature review of academic studies, industrial applications, and real-world deployments from 2018 to 2025, providing a practical guide and detailed overview of modern Retrieval-Augmented Generation (RAG) architectures. RAG offers a modular approach for integrating external knowledge without increasing the capacity of the model as LLM systems expand. Research and engineering practices have been fragmented as a result of the increasing diversity of RAG methodologies, which encompasses a variety of fusion mechanisms, retrieval strategies, and orchestration approaches. We provide quantitative assessment frameworks, analyze the implications for trust and alignment, and systematically consolidate existing RAG techniques into a unified taxonomy. This document is a practical framework for the deployment of resilient, secure, and domain-adaptable RAG systems, synthesizing insights from academic literature, industry reports, and technical implementation guides. It also functions as a technical reference.

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