What does this research mean for the field?

Retrieval-Augmented Generation (RAG) systems rely on a complex architecture integrating retrieval pipelines with large language models, but currently face significant practical limitations including retrieval failures, hallucinations, and scalability challenges. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to provide a detailed overview of Retrieval-Augmented Generation (RAG) systems, examining their architecture and challenges.

July 2, 2026Open Access

Review of Retrieval-Augmented Generation (RAG) System Architecture, Limitations, and Practical Challenges

Key Points

The aim is to provide a detailed overview of Retrieval-Augmented Generation (RAG) systems, examining their architecture and challenges.
Review of existing academic literature and industry standards on RAG systems.
Analysis of architecture, retrieval pipeline, and embedding models.
Discussion of limitations such as context fragmentation and security issues.
Identified key architectural elements of RAG systems including retrieval pipelines and embedding models.
Highlight significant limitations including retrieval failures and latency issues.
Synthesize recommendations for future research directions in RAG technology.

Abstract

This paper presents a comprehensive review of Retrieval-Augmented Generation (RAG) systems, focusing on their architecture, retrieval pipeline, indexing mechanisms, embedding models, vector databases, and large language model integration. It also discusses key limitations including retrieval failures, hallucinations, context fragmentation, latency, scalability, security, privacy, and evaluation challenges. The paper synthesizes findings from publicly available academic literature and industry standards to provide students, researchers, and practitioners with an overview of current RAG systems and future research directions.

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