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Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability.
https://github.com/langchain-ai/rag-from-scratch
🇪🇦#Spagna, elezioni in Castiglia e León: i popolari guidati dal presidente Alfonso Fernandez #Mañueco riconfermano il governo regionale. Sia i socialisti che Vox tengono a discapito dei provincialisti, oltre a Podemos e Ciudadanos che escono dal consiglio.
#PP (centrodestra): 35,5% (33 seggi)
#PSOE (centrosinistra): 30,7% (30)
#Vox (destra): 18,9% (14)
#UPL (regionalisti di León): 4,4% (3)
#XAV (provincialisti di Ávila): 0,9% (1)
#SY (provincialisti di Soria): 0,7% (1)
Affluenza: 59,7% (+0,9)