Usporedite metode

	Retrieval-Augmented Generation (RAG)	Konstrukcija grafova znanja iz teksta
Područje	Rudarenje teksta	Rudarenje teksta
Obitelj	Process / pipeline	Process / pipeline
Godina nastanka	2020	—
Tvorac	Lewis, Patrick et al. (Meta AI / Facebook AI Research)	—
Vrsta	Hybrid retrieval + generation pipeline	Structured knowledge representation pipeline
Temeljni izvor	Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459-9474.	Hogan, A. et al. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4), 1-37.
Drugi nazivi	RAG, retrieval-augmented LLM, grounded generation, Erişim Destekli Metin Üretimi (RAG)	knowledge graph, KG construction, Bilgi Grafiği Oluşturma (Knowledge Graph)
Srodne	7	3
Sažetak	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.	Knowledge graph construction is a text-mining pipeline that turns unstructured text into a structured graph of entities and the relations between them. Drawing on the synthesis of Hogan et al. (2021) and the relational-machine-learning review of Nickel et al. (2016), it represents knowledge as nodes (entities such as people, places, organisations) connected by labelled edges (relations), and serves semantic search, recommendation systems, and reasoning.