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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 #4286 · 03/02/2025, 01:00 PM

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CZ Proposes New Token Unlock Model 🔵 CZ proposes a tokenomics model linking unlocks to price growth and time, reducing market impact. 😏 Initial 10% sold for development. 😏 Unlock every 6 months, contingent on 2x price increase maintained for 30 days. 😏 Max 5% unlock each time. ❓ Market implications include lack of sell pressure from mass unlocks and encouraging long-term projects. 🔴 Critics argue it could disadvantage small teams reliant on token price. Could this model reshape token economies? Read more: Link #CZ#Tokenomics#Crypto#Blockchain#Ethereum#MarketTrends#Investing#DeFi#Finance#Startups#Innovation#LongTerm#SmartContracts#PriceGrowth#Unlocks#Community#PricingModel#InvestmentStrategies#ElonMusk#HongKong