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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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@airdropsalertteam · Post #6847 · 06/10/2022, 01:23 PM

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⚔️AMAZING PVE GAMEPLAY IN ORBITAU ⚔️ 💠 Testnet for Orbitau will be available shortly. In order to help you prepare, here is a very attractive gameplay clip from Chapter 1 of Map Foras. 🚦 Reveal more about Orbitau's gameplay: https://blog.orbitau.io/post/amazing-pve-gameplay 💡 Be sure to stay tuned for updated information about the upcoming testnet. ⚡️ BUT FIRST, FILL YOUR WALLET WITH SUFFICIENT $TAUM TO BUY IMMORTAL EGGS THAT WILL BE ON SALE THIS MARCH 14! #Orbitau#PvE#gameplay#ORI#TAUM ➖➖➖➖➖➖➖➖➖➖ 📌📌Our channels: 🌐Orbitau Website | 🐦Twitter| 🌎Orbitau Announcements | 🌎Orbitau Global | 👉Facebook |👉Discord | 👉Reddit | 👉Medium ➖➖➖➖➖➖➖➖➖➖ 📌Orbitau local communities: Vietnam🇻🇳 | Brazil🇧🇷 | Bangladesh🇧🇩 | Thailand🇹🇭 | Indonesia🇮🇩 | Philippines🇵🇭 | Arab🇸🇦 | Korea🇰🇷 | Japan🇯🇵 | India🇮🇳 | Turkey🇹🇷 | China🇨🇳

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