#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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Reports say a staff mistake during an airdrop sent 2,000 $BTC($133M) instead of a small KRW reward.
Some users sold it right away, causing the price to drop fast.
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Reports say a staff mistake during an airdrop sent 2,000 $BTC($133M) instead of a small KRW reward.
Some users sold it right away, causing the price to drop fast.
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