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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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Addis Standard

@addisstandardeng · Post #21595 · 03/03/2026, 03:29 PM

Grand #Ethiopian Renaissance Dam: how #Africa’s largest hydropower project Is powering #Ethiopia’s future A detailed examination of the Grand Ethiopian Renaissance Dam – its construction, specifications, commissioning, regional impact, and remaining challenges. The Grand Ethiopian Renaissance Dam (#GERD) is the largest hydropower project in Africa. Located on the #Blue_Nile in Ethiopia’s #Benishangul-Gumuz region, about 14km from the Sudanese border, the project has been delivered by Ethiopian Electric Power (#EEP) with major civil works undertaken by #Webuild Group (formerly #Salini Impregilo). The objective of the dam is to increase Ethiopia’s electricity generation capacity, reduce dependence on imported energy, and support long-term industrial and economic development. Construction began in 2011, and Ethiopia officially inaugurated...... Read more: https://www.waterpowermagazine.com/analysis/grand-ethiopian-renaissance-dam-how-africas-largest-hydropower-project-is-powering-ethiopias-future/?cf-view