Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is a natural-language-processing pipeline introduced by Lewis et al. in 2020 that strengthens a large language model (LLM) with evidence fetched at inference time from an external knowledge base. Instead of relying solely on what a model memorised during training, RAG first retrieves the most relevant passages from a document index and then hands those passages to the LLM as context, grounding the generated answer in verifiable, up-to-date information. The approach reduces hallucination and allows domain-specific or time-sensitive knowledge to be injected without retraining the model.
Allikakirje
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- Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459-9474. · DOI 10.48550/arXiv.2005.11401
- Gao, Y. et al. (2023). Retrieval-Augmented Generation for Large Language Models: A Survey. arXiv preprint. · DOI 10.48550/arXiv.2312.10997
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