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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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@premium_trading_crypto_signals · Post #3256 · 03/26/2024, 11:55 AM

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#GAL price is forming a wedge pattern. Its upward trend has slowed down and if the price can break the pattern downwards, it can dump up to $4.4. @Altcoin_Charts

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@signal_bitcoins · Post #2713 · 02/08/2024, 04:36 PM

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#GAL price is too close to the supply zone on 3D Time frame,we expect a good correction from this area 📉 ❄️@signals_bitcoin_crypto❄️ ❄️@Shadow_support0o❄️

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