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	Generazione Aumentata da Recupero (RAG)	BERT Embeddings
Campo	Text mining	Text mining
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	2020	2019
Ideatore	Lewis, Patrick et al. (Meta AI / Facebook AI Research)	Devlin, Chang, Lee & Toutanova (Google AI)
Tipo	Hybrid retrieval + generation pipeline	Contextual transformer text-representation method
Fonte seminale	Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459-9474.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.
Alias	RAG, retrieval-augmented LLM, grounded generation, Erişim Destekli Metin Üretimi (RAG)	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri
Correlati	7	4
Sintesi	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.	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.