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

TL;DR

This comprehensive review identifies how Retrieval-Augmented Generation architectures have evolved from canonical DPR+FiD-type pipelines to agentic, graph-augmented, and multimodal systems. It provides a taxonomy across retrieval strategies, fusion topologies, knowledge modalities, trust calibration, and adaptive pipelines, and catalogs key innovations like RAG-Fusion, RE-RAG, GraphRAG, and AutoRAG. The paper couples architectural analysis with rigorous evaluation frameworks, best practices, and safety/governance considerations, outlining frontier directions such as end-to-end differentiable training, RLHF co-evolution, and multi-agent collaboration. Its findings underscore the practical impact of modular RAG systems for enterprise knowledge tasks, emphasizing standardized benchmarks, governance, and robust trust mechanisms to realize scalable, trustworthy deployments.

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.