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Source channel @githubtrending · Post #14993 · Jul 24
#jupyter_notebook 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
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@ai_match_prediction · Post #12107 · 05/15/2026, 01:05 PM
Scotland Premiership🏴 2026-05-15 18:45 UTC Partick vs. Dunfermline Predicted outcome: Dunfermline Predicted score: 1:2 Current: 0:1 37' #Partick#Dunfermline#Live #Football#ScotlandPremiership
@ai_match_prediction · Post #12065 · 05/12/2026, 01:05 PM
Scotland Premiership🏴 2026-05-12 18:45 UTC Kilmarnock vs. Dundee Predicted outcome: Kilmarnock✅ Predicted score: 2:1 Actual: Kilmarnock 3:1 #Kilmarnock#Dundee 2026-05-12 18:45 UTC Aberdeen vs. ST Mirren Predicted outcome: Aberdeen❌ Predicted score: 2:1 Actual: ST Mirren 0:2 #Aberdeen#STMirren 2026-05-12 18:45 UTC Dundee Utd vs. Livingston Predicted outcome: Dundee Utd❌ Predicted score: 2:1 Actual: Draw 0:0 #DundeeUtd#Livingston 2026-05-12 18:45 UTC Dunfermline vs. Partick Predicted outcome: Dunfermline❌ Predicted score: 2:1 Actual: Draw 1:1 #Dunfermline#Partick #Football#ScotlandPremiership