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	Retrieval-Augmented Generation (RAG) (Generowanie Wzbogacone o Wyszukiwanie)	Transformer (NLP)
Dziedzina	Eksploracja tekstu	Uczenie głębokie
Rodzina	Process / pipeline	Machine learning
Rok powstania	2020	2017
Twórca	Lewis, Patrick et al. (Meta AI / Facebook AI Research)	Vaswani, A. et al.
Typ	Hybrid retrieval + generation pipeline	Attention-based deep neural network
Źródło pierwotne	Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459-9474.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
Inne nazwy	RAG, retrieval-augmented LLM, grounded generation, Erişim Destekli Metin Üretimi (RAG)	Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP
Pokrewne	7	4
Podsumowanie	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.	The Transformer is an attention-based deep learning model, introduced by Vaswani and colleagues in 2017, that performs text classification, named-entity recognition, and language modelling by letting every token in a sequence attend directly to every other token. It replaced earlier recurrent designs with a self-attention mechanism that processes whole sequences in parallel.