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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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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