#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
#SHIB/USDT analysis :
The price of #SHIB has recently broken out of its trendline, indicating a potential bullish momentum. While a retracement to the 200 EMA is expected, the overall sentiment suggests that the price is likely to move upwards and test the swing high level.
TF : 4h
Entry : $0.00001480
Target : $0.00001734
SL : $0.00001313
#SHIB/USDT analysis :
#SHIB has successfully broken out of the trendline, demonstrating a strong bullish movement. It is anticipated that this upward momentum will continue, allowing the price to test higher levels.
TF : 1D
Entry : $0.00002536
Target : $0.00004567
SL : $0.00001827
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