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Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis et al. (Meta AI) • 2020
RAGRetrievalKnowledge Bases
Abstract
The original RAG paper. Lewis et al. proposed combining pre-trained language models with a retrieval mechanism that accesses external knowledge at generation time. This architecture became the foundation of every enterprise 'chat with your data' application, enabling models to ground their outputs in specific, verifiable information sources.
Why It Matters
- Defined the RAG architecture now used across enterprise AI
- Enabled grounded generation from external knowledge bases
- Directly relevant to modern AI product development
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Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis et al. (Meta AI) • 2020
RAGRetrievalKnowledge Bases
Abstract
The original RAG paper. Lewis et al. proposed combining pre-trained language models with a retrieval mechanism that accesses external knowledge at generation time. This architecture became the foundation of every enterprise 'chat with your data' application, enabling models to ground their outputs in specific, verifiable information sources.
Why It Matters
- Defined the RAG architecture now used across enterprise AI
- Enabled grounded generation from external knowledge bases
- Directly relevant to modern AI product development
Ask about this paper
Loading chat...