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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
⚠️ A whale who supplied 56,995 $WETH ($90.8M) to borrow $DAI on #Maker is on the verge of liquidation, with a liquidation price of $1,564.58.
Earlier today, another giant whale was already liquidated for 67,569 $ETH ($106M) at $1,650 to repay a $74.49M loan as the price plunged!
In the past 24 hours, $898M, mostly from long positions, was liquidated from the cryptocurrency market.
Follow @spotonchain for more insights at https://x.com/spotonchain/status/1909075294834848057