#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
EVAA: Introducing Loop APY for LP Pool Interface
#Loop#EVAA
EVAA introduces a new Loop APY feature in its LP Pool Interface, enabling users to deposit LP tokens from StormTrade or DeDust as collateral, borrow TON or USDT, and utilize a liquidity looping strategy to potentially enhance annual returns. This strategy combines third-party yields, EVAA rates, and compounding effects.
Source: link
@tonlines
For operatori
Umuman olganda kod yozayotganingizda bir xil hisoblash jarayonini qayta-qayta yozish qimmatli vaqtingizni o'g'irlab sizni bezor qilishi mumkin, masalan siz “Salom, Dunyo!” jumlasini 100 marta yozishingiz zarur bo’lib qoldi.Siz uni qayta qayta yozib chiqgan bo’larmidingiz, yo’q albatta.
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👨🏫 Mentor: Suxrob Xayitmurodov
#csharp#for#loop#starter
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