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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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@V1_BLOG · Post #2363 · 02/20/2021, 04:14 PM

测速机场: ImmTelecom 测速宽带: 北方移动700M 测速时间: 2.21 官网: https://immtel.com 清晰测速图: https://t.me/V1blog_g/57111 测速By-V1测速组 #ImmTelecom 新开的机场,面板用的与佩奇同款,但线路与佩奇无关,稳定性有待观察。 有需要可自行加入 @immtelecom@immtelecom_chat 频道群组 投稿(暂不接受)

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