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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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@tokenpocket_channel · Post #1587 · 04/17/2025, 03:35 AM

🆕We just released TokenPocket’s New Version on Chrome. 🔺2.1.0🔺 🔵Support pre-execution analysis for trading. 🔵Support for custom gas fee settings. 🔵Support for enabling/disabling #Permit signature. 🔵Support for executing voting, claiming rewards, and other operations in TRON Multi-Signature wallets. 🔵Optimize the system settings page. 🔵Optimize the approval reminders. 🔵Optimize the network management page. Add TokenPocket to your Chrome. 👉https://extension.tp.xyz

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@tokenpocket_channel · Post #960 · 12/02/2023, 07:30 AM

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