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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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SLEX NEWS

@slex_io · Post #163 · 02/23/2024, 09:21 AM

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🤝New Exciting Partnership: SLEX Exchange Has Joined the Syscoin Ecosystem🥳 ☄️We're thrilled to announce that SLEX Exchange is now a member of the Syscoin ecosystem. It supports low-cost transactions, high throughput, and a variety of decentralized finance (DeFi) applications, making it an attractive ecosystem for developers and users alike✨ 📊Joining the Syscoin ecosystem is part of our broader initiative to support a diverse range of blockchains, enhancing the trading experience for our users. We're excited to support the following blockchains, each bringing unique strengths to the table: 🔗Scroll 🔗Zora 🔗Arbitrum 🔗Optimism 🔗zkSync 🔗Linea 🔗Base 🔥Stay tuned for more updates as SLEX will continue to grow and evolve, providing new exciting features for efficient trading! #SLEXExchange#SyscoinEcosystem#BlockchainDiversity#CryptoTrading#Innovation#FutureFinance

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