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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
#Sentiment notes that the activity of #BTC discussion on social media has risen to a 4-month high, there is a serious panic among the crowd. Such depressive indicators in the moods usually preceded the rebounds.
⚡️ Sentiment notes a decrease in the number of wallets with a non-zero balance 🪙#BTC, which may mean that some participants will enter the cache before the US elections. The experts of #Sentiment consider this development of the situation to be #bullish for #BTC after the elections are held...
#Sentiment notes a sharp rise in bullish sentiment in #Solana
there are rumors that #Apple (#AAPL) is starting to use the Solana — Sentiment blockchain
🤣Ethereum Fear & Greed Index has dropped to 13, signaling Extreme Fear across the market.
市場情緒跌入 極度恐懼區間,代表短期資金風險偏好明顯下降,投資人情緒趨於保守。
#Crypto#以太坊#币圈#区块链
⚡️ 歷史上極度恐懼區域,往往出現在市場接近階段性底部時,但短線波動仍可能持續。
#Ethereum#Markets#Sentiment
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