#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
#ROSE/USDT analysis :
#ROSE is in an uptrend, forming higher highs and higher lows above the 200ema. The price is expected to sustain its bullish momentum and test new highs. Wait for a price retracement over the support zone for a long entry.
TF : 2h
Entry : $0.0606
Target : $0.0696
SL : $0.0561
#ROSE +%33 in just 5 days 🤑🤑
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