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Source channel @githubtrending · Post #14826 · Jun 12

#jupyter_notebook#ai#llm#llms#multi_modal#openai#python#rag Retrieval-Augmented Generation (RAG) is a technique that helps improve the accuracy of large language models by fetching relevant information from databases or documents. This approach ensures that the model's responses are based on up-to-date and accurate data, reducing errors and "hallucinations" where the model might provide false information. For users, RAG offers more reliable and trustworthy responses, allowing them to verify the sources used to generate those responses. This method also saves resources by avoiding the need to retrain models with new data. https://github.com/FareedKhan-dev/all-rag-techniques

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Venture Village Wall 🦄

@venturevillagewall · Post #4282 · 03/02/2025, 01:00 AM

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Bitcoin: Panic Buying Amid Market Shakeup Panic reactions in the market are evident as Bitcoin fluctuates. Key developments in the TON ecosystem include: - TVM Ventures launching a $100M fund for TON growth 🔗 - Updates from TON Core for Dec 2024 - Jan 2025 🔗 - Steve Yun discusses DeFi’s future in Telegram on CNBC 🔗 - First mid-core web3 game launched on TON 🔗 - Support program for Mini Apps initiated 🔗 - Innovations from Notcoin team highlighted 🔗 - LayerZero’s new USDT bridge enhances TON’s cross-chain capabilities 🔗 - Key investments in DeFi and SocialFi detailed by TVM Ventures 🔗 - TON’s ecosystem expands beyond major project monopolies 🔗 - TRM Labs introduces a comprehensive blockchain analysis tool for TON 🔗 - The domain believers.ton is permanently lost 🔗 #Bitcoin#DeFi#TON#Crypto#Web3#Investment#LayerZero#Blockchain#MiniApps#PanicBuying#TVMVentures#Notcoin#TRMLabs

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