Retrieval-Augmented Generation (RAG) enhances LLMs with external knowledge

Retrieval-Augmented Generation (RAG) is an AI technique that enhances Large Language Models (LLMs) by enabling them to access and utilize external knowledge sources. Developed in 2020 by researchers from Facebook AI Research, University College London, and New York University, RAG addresses LLM limitations such as outdated knowledge and the tendency to hallucinate. Vector RAG, a specific implementation, uses numerical embeddings to find semantically similar information, improving response accuracy and relevance across applications like customer support, education, and healthcare. AI

IMPACT Enhances LLM accuracy and relevance by integrating external, up-to-date information, addressing core limitations like outdated knowledge and hallucinations.

RANK_REASON The item describes a technical approach (RAG) and its implementation (Vector RAG), referencing a specific research paper and its authors. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• Advances in Neural Information Processing Systems 33
• Facebook AI Research
• New York University
• Patrick S H Lewis
• retrieval-augmented generation
• University College London
• vector RAG
• Wikipedia

• product
• paper

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