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

TL;DR

This paper systematically reviews Retrieval-Augmented Generation (RAG) architectures, methodologies, and trust frameworks, providing a unified taxonomy and practical deployment guidelines for resilient and domain-adaptable RAG systems.

Contribution

It consolidates diverse RAG techniques into a comprehensive taxonomy and offers a practical framework for deploying secure, trustworthy, and adaptable RAG systems.

Findings

Quantitative assessment frameworks for RAG

Analysis of trust and alignment implications

Unified taxonomy of RAG methodologies

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.