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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 #3758 · 12/31/2024, 07:00 AM

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2023 Reflections and Initiatives Key takeaways from the past year: Emphasis on founder-side roles, collaborations with dynamic teams, and growth initiatives. Initiated two accelerators: triangle.tg connecting TON to Web3, supporting 11 teams with notable backers like Binance Labs, GSR, and others. Launched Gaming.tg with Anton from Helika.io, focusing on the gaming sector. Significant partnerships emerged, enhancing the value for founders. Currently supporting promising Telegram ecosystem projects, anticipating a return to article writing in 2025. Appreciating the support received and lessons learned. #FounderSide#Web3#TON#Accelerator#Gaming#BinanceLab#GSR#Polychain#DelphiDigital#Blackrock#TriangleTG#GamingTG#Community#Innovations#Telegram#Investments#2023Reflections#CholyLabs#Projects#Support#Partnerships#Leadership