Compară metode

	Generare de Limbaj Natural	Generare Augmentată prin Regăsire (RAG)
Domeniu	Mineritul textelor	Mineritul textelor
Familie	Process / pipeline	Process / pipeline
Anul apariției	1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era)	2020
Autorul original	Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018)	Lewis, Patrick et al. (Meta AI / Facebook AI Research)
Tip	NLP generative task — structured data to natural language	Hybrid retrieval + generation pipeline
Sursa seminală	Gatt, A. & Krahmer, E. (2018). Survey of the State of the Art in Natural Language Generation: Core Tasks, Applications and Evaluation. Journal of Artificial Intelligence Research, 61, 65-170.	Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459-9474.
Denumiri alternative	NLG, data-to-text, text generation, Doğal Dil Üretimi (NLG)	RAG, retrieval-augmented LLM, grounded generation, Erişim Destekli Metin Üretimi (RAG)
Înrudite	7	7
Rezumat	Natural Language Generation (NLG) is the branch of natural language processing that automatically produces fluent, human-readable text from structured data, knowledge graphs, or semantic representations. Formalised in the classical pipeline by Reiter and Dale (2000) and surveyed comprehensively by Gatt and Krahmer (2018), NLG powers applications ranging from automated financial reporting and weather bulletins to data storytelling and conversational agents.	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.