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About this paper
Agada Joseph Oche, Ademola Glory Folashade, Tirthankar Ghosal, Arpan Biswas
A systematic review chronicles the evolution, technical components, applications, challenges, and future directions of Retrieval-Augmented Generation (RAG) systems, synthesizing progress and identifying gaps from its inception to projected advancements in mid-2025. It provides a comprehensive, structured understanding for researchers navigating this rapidly expanding field.
Key insights
- RAG significantly enhances LLM reliability by grounding outputs in verifiable sources and dynamically updating knowledge, mitigating hallucinations and improving factual consistency.
- Its modular architecture enables efficient and secure integration with proprietary data, fostering enterprise AI adoption without compromising privacy or requiring frequent model retraining.
- The field consistently addresses challenges related to retrieval quality, latency, scalability for massive corpora, and developing robust evaluation methodologies that assess factual consistency and grounding.
Summary by alphaXiv.
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