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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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@rushousebrussels · Post #2085 · 08/27/2024, 01:19 PM

🔊Sound on ☕ please, repeat Привет! Tired of learning Russian? Great news for you today then: we have thousands of words that are similar to English ones! Let’s start with those relating to food! Notice that the pronunciation is not always similar, so watch out for those accents! Still, learning Russian versions of the words you already know is far easier than learning absolutely new ones 🙂 That’s not the end of the list! Do you have any more examples? Drop them down in the comments! 👇🏼 Credits: Westend61, Aleksandr Kuzmin, Martin Barraud, serggn, Chris Stein, Halfdark/Getty Images Sourse: Russia Beyond https://t.me/russiabeyond/8927 #russianclasses#russianbeyond#РусскийДом#RussianHouse#Образование#КРЯ#brussels#belgium#educationrussia#RussianHouseBrussels#russianlanguagecoursesbrussels#russianlanguage#learnrussian#studyrussian