#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
#ELF/USDT analysis :
#ELF is currently in an uptrend, forming higher highs (HHs) and higher lows (HLs). Following an impulsive move, the price has retraced to the 200 EMA and a key support zone. It is anticipated that the price will rebound from this level and resume its bullish momentum, aiming to test the previous swing high.
TF : 1D
Entry : $0.4500
Target : $0.8400
SL : $0.3550
#ELF/USDT analysis :
#ELF is currently consolidating above the previously respected support zone. The price is expected to bounce back from this level and continue its bullish rally, potentially testing previous highs.
TF : 1W
Entry : $0.4124
Target : $0.7853
SL : $0.3060