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Source channel @githubtrending · Post #14993 · Jul 24

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#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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@panguliaowang · Post #17591 · 04/10/2020, 06:31 AM

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#朱万利 20200404 #SV#龚小夏 龚晓夏SV案 法庭质证证词 2-1 内涉一些往事档案 同内容MP3 https://t.me/fenghuangniepan/2920 https://www.youtube.com/watch?v=QMk_3odpba0

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@panguliaowang · Post #17592 · 04/10/2020, 06:32 AM

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#朱万利 20200404 #SV#龚小夏 龚晓夏SV案 法庭质证证词 2-2 内涉一些往事档案 ，包括 #政事小哥 同内容MP3 https://t.me/fenghuangniepan/2921 https://www.youtube.com/watch?v=S5m1LqxXTpw

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