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Source channel @githubtrending · Post #14993 · Jul 24

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#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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GRAYKOLORI 🧪💀

@graykolori · Post #129 · 06/14/2024, 04:19 PM

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Помимо Мелинои у меня сейчас капец много чего в работе, скоро надеюсь доделаю вот такую мадам или это парень, я пока сам не понял....., хотя обычно у меня то уж всё поняяяяятно, но вот так получилось🧐 Почему то ловлю вайбы ГП и Холмса #witch#art#3dart#alexgray

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