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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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🎨 𝗧𝗘𝗠𝗔𝗦 𝗧𝗘𝗟𝗘𝗚𝗥𝗔𝗠 ➤ 𝗧𝗚𝗫

@DNZ_Temas · Post #3269 · 02/03/2024, 08:53 PM

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🎨 𝗧𝗘𝗠𝗔𝗦 𝗧𝗘𝗟𝗘𝗚𝗥𝗔𝗠 ➤ 𝗧𝗚𝗫 📢 𝗖𝗵𝗮𝗻𝗻𝗲𝗹: @DNZ_Temas ➖ ➖ ➖ ➖ ➖ ➖ ➖ ➖ ➖ ➖ 🔗𝗔𝗣𝗣𝗟𝗬 𝗧𝗛𝗘𝗠𝗘>> ✅ ⤵️𝗕𝗔𝗖𝗞𝗚𝗥𝗢𝗨𝗡𝗗>> ⬇️𝗪𝗔𝗟𝗟𝗣𝗔𝗣𝗘𝗥>> ➖ ➖ ➖ ➖ ➖ ➖ ➖ ➖ ➖ 🏷️Tags: : #Watermelon#Red #Green#MDGram ╚═══ ღ 𝔻ℕℤ 𝕋𝔼𝕄𝔸𝕊 ღ ═══╝

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🎨 𝗧𝗘𝗠𝗔𝗦 𝗧𝗘𝗟𝗘𝗚𝗥𝗔𝗠 ➤ 𝗧𝗚𝗫

@dnz_temas · Post #2405 · 08/16/2023, 02:56 AM

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📱 𝐃𝐍𝐙 𝐓𝐄𝐌𝐀𝐒 🔠🔠 >> 🔠🔠🔠 📢 𝗖𝗵𝗮𝗻𝗻𝗲𝗹: @DNZ_Temas ➖➖➖➖➖➖➖➖➖➖➖➖ 🔗𝗔𝗣𝗣𝗟𝗬 𝗧𝗛𝗘𝗠𝗘>> 𝕋𝔼𝕃𝔼𝔾ℝ𝔸𝕄 ✅ ⤵️𝗕𝗔𝗖𝗞𝗚𝗥𝗢𝗨𝗡𝗗>> ⬇️𝗪𝗔𝗟𝗟𝗣𝗔𝗣𝗘𝗥>> ➖➖➖➖➖➖➖➖➖➖➖➖ 🏷️Tags: #Fruit#Watermelon ╚════ ღ 𝔻ℕℤ 𝕋𝔼𝕄𝔸𝕊 ღ ════╝

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