#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
#POND/USDT analysis :
#POND has shown a strong recovery from the support zone following a significant decline of -77% from its all-time high (ATH). The price has also successfully broken above the trendline that was previously acting as resistance, indicating a likely continuation of its bullish momentum toward reaching new all-time highs. A potential gain of +120% is anticipated from the current levels.
TF : 1W
Entry : $0.01966
Target : $0.04240
SL : $0.01358
#POND👀
CoinLegs algorithm detected Symmetrical Triangle at 1h chart 📊
Seems like we got a breakout. Stop Loss below the trendline 🛑
Target levels are on the charts.
#POND result
3rd target achieved in just 12 days ✅✅✅
One more huge quick profit 21.9%🤑💰🤑
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#POND result
1st target achieved in just 1 day✅
One more quick profit 7%💰🤑
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