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Source channel @githubtrending · Post #14826 · Jun 12

#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

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Venture Village Wall 🦄

@venturevillagewall · Post #3839 · 01/10/2025, 04:00 PM

Crypto Industry Echoes Dotcom Boom 🔍 A new report from BloFin indicates that the crypto market mirrors the dotcom boom's phase in the US. Increased liquidity is observed, with monetary conditions easing, hinting at a potential altcoin season. Experts believe Bitcoin and Ethereum may lead market movements based on liquidity trends and general economic conditions. For an in-depth analysis, click here. Furthermore, recent volatility shows cryptocurrency's risks due to inflation concerns tied to potential political changes in the US. Two scenarios are examined: bullish expectations from liquidity influx versus bearish forecasts stemming from inflationary risks and market overexcitement. A detailed breakdown is available in the full analysis. Also, CryptoQuant signals a 'buy on dip' for Bitcoin as large investors increase purchases. Learn more about the latest developments here. #Crypto#Bitcoin#Altcoins#Liquidity#MarketTrends#DotcomBoom#Investments#EconomicGrowth#Inflation#PoliticalRisk#QuantAnalysis#InvestmentStrategies#USMarkets#DigitalAssets#Volatility#AI#VC