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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
Bitcoin ETFs See Major Inflow Reversal
On January 15, Bitcoin spot ETFs recorded a net inflow of $755 million, marking the first inflow after four days of outflows. The Fidelity ETF (FBTC) led the charge, attracting $463 million. Meanwhile, Ethereum products also saw inflows, totaling $59.78 million.
Forecasts from HashKey Group predict Bitcoin could hit $300,000 by 2025 and Ethereum $8,000, with overall market cap reaching $10 trillion. Analyst insights suggest the Litecoin ETF may be next for approval in the US.
For more details, visit the link.
#Bitcoin#ETF#Cryptocurrency#Ethereum#Investing#Finance#MarketTrends#HashKey#Litecoin#Fidelity#CryptoForecast#AI#VC