#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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Direction: SHORT 📉
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Profit : 25.71% with 10x Leverage
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We are excited to announce that @HaedalProtocol has officially completed the Sui smart contract audit conducted by @MoveBit_ , an expert and leading security firm on the #Move ecosystem.
https://twitter.com/HaedalProtocol/status/1696100358735733089?s=20
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🎉 В Movement объявили о запуске собственного стейблкоина USDCx – анонс
USDCx обеспечен USDC в соотношении 1:1.
Ранее: L1-блокчейн от Movement
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