#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
🪙EthGlobalNews | #BTC Corporate Holdings Update
📊 Michael Saylor’s MicroStrategy still tops the list with 640,808 BTC, yet its dominance has slipped to 60% as more companies join the treasury wave.
🟠企業持幣版圖持續擴張,越來越多上市公司將比特幣納入資產負債表,集中度正在下降。
⚡️ 機構化持幣結構轉向多元,顯示「企業級 FOMO」已開始蔓延。
#比特币#Institutions#Markets#Insight
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