قارن الطرق

	التوليد المعزز بالاسترجاع (RAG)	الإجابة على الأسئلة (QA)
المجال	تنقيب النصوص	تنقيب النصوص
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	2020	—
صاحب الطريقة	Lewis, Patrick et al. (Meta AI / Facebook AI Research)	—
النوع	Hybrid retrieval + generation pipeline	NLP text-comprehension task
المصدر التأسيسي	Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459-9474.	Rajpurkar, P. et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP.
الأسماء البديلة	RAG, retrieval-augmented LLM, grounded generation, Erişim Destekli Metin Üretimi (RAG)	QA, machine reading comprehension, Soru Cevaplama (Question Answering)
ذات صلة	7	4
الملخص	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.	Question answering is a natural-language-processing task that automatically answers natural-language questions grounded in a given context passage, using either extractive or generative approaches. The task was crystallised by the SQuAD benchmark of Rajpurkar et al. (2016), and later models such as XLNet (Yang et al., 2019) pushed reading-comprehension accuracy higher.