#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
#AUCTION/USDT analysis :
#AUCTION has tested the 200 EMA and rebounded from it. The price has broken out of the trendline, confirming the continuation of the bullish trend. The price is anticipated to test the swing high level.
TF : 30min
Entry : $33.35
Target : $37.30
SL : $30.98
#AUCTION/USDT analysis :
#AUCTION is in an uptrend, forming higher highs (HHs) and higher lows (HLs). The price has recently broken out of the trendline, indicating a potential resumption of its bullish momentum. It is anticipated that the price will continue to rise and test previous highs.
TF : 30min
Entry : $20.05
Target : $21.53
SL : $19.06