#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
Bitcoin on #Bithumb suddenly dropped, trading over 10% below other markets.
Reports say a staff mistake during an airdrop sent 2,000 $BTC($133M) instead of a small KRW reward.
Some users sold it right away, causing the price to drop fast.
JUST IN : 💰🚨Bitcoin on #Bithumb suddenly dropped, trading over 10% below other markets.
Reports say a staff mistake during an airdrop sent 2,000 $BTC($133M) instead of a small KRW reward.
Some users sold it right away, causing the price to drop fast.
➖➖➖➖➖➖➖➖➖
📣@cryptonewstel
✨Vip join⭐️
🚨 DWF Labs has deposited all 170K $CYBER to #Bithumb at $8.6 on average ($1.46M) in 7 transactions over the past 24 hours.
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👉 More details: https://platform.spotonchain.ai/signal-details/dwf-labs-closed-the-first-cyber-deal-for-great-profit-516
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