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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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Pocket Rocket Announcement

@pocket_rocket_game · Post #12 · 05/28/2024, 10:04 AM

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🚀Our spaceship is on its way to success 🚀 And we believe that each of you can help us reach new heights. That’s why we are launching a content creation activity! The participants will receive 500,000 crystal rocks each for their videos. We will also select the best-performing entries and reward them with WBT. 📲You can join the activity on Instagram, TikTok, X (Twitter), or YouTube. Fill out this form to participate. Read more about the activity here. ⚡️ Don’t forget to use the #PocketRocket hashtag when publishing your content. We can’t wait to see your content! Play | Announcements | Telegram Chat | Twitter |

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