#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
#OXT/USDT analysis :
#OXT has broken out and retested a previously respected support zone. Price is expected to bounce back from this level and test previous highs. This indicates a potential continuation of the bullish trend, as the retest confirms the strength of the support.
TF : 1D
Entry : $0.1070
Target : $0.1570
SL : $0.0857
#OXT finally broken up the trading range on 8H Time frame,we will have good pump again after a pullback,let's see what will happen 💫
❄️@signals_bitcoin_crypto❄️
❄️@Shadow_support0o❄️
#OXT result
2 nd target achieved in just 3 days ✅✅
One more huge quick profit 15%🤑💰🤑
👉 Still thinking? The more you wait more you lose profit
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#OXT has good bullish momentum on Weekly time frame,we are waiting for a pullback and rebuy,buy dip
📈
❄️@signals_bitcoin_crypto❄️
❄️@Shadow_support0o❄️