#jupyter_notebook#ai#llm#llms#multi_modal#openai#python#rag
Retrieval-Augmented Generation (RAG) is a technique that helps improve the accuracy of large language models by fetching relevant information from databases or documents. This approach ensures that the model's responses are based on up-to-date and accurate data, reducing errors and "hallucinations" where the model might provide false information. For users, RAG offers more reliable and trustworthy responses, allowing them to verify the sources used to generate those responses. This method also saves resources by avoiding the need to retrain models with new data.
https://github.com/FareedKhan-dev/all-rag-techniques
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.
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#Bitcoin#ETF#Cryptocurrency#Ethereum#Investing#Finance#MarketTrends#HashKey#Litecoin#Fidelity#CryptoForecast#AI#VC