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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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Ethio-Djibouti Railway S.C.

@ethiodjiboutirailway · Post #1360 · 01/25/2024, 05:06 AM

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Congratulations to the fourteen train driver trainees who have successfully completed their training! January 24, 2024(EDR); During their one year training period, the trainees have successfully finished their theoretical and practical training. That provided by the Ethiopian-Djibouti Railway Capacity Building program’s fourth round. Today, they took their Ministry of Transport and Logistics qualification exam, and now one step closer to obtaining their licenses and starting their careers as train drivers. We are incredibly proud of their hard work and commitment, and we wish them all the best in their future endeavors! #EthiopiaDjiboutiRailway#EthiopiajiboutiRailwayfamily#CapacityBuilding #TrainDriverTraining#Congratulations

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