#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
#COTI/USDT analysis :
#COTI is currently forming higher highs (HHs) and higher lows (HLs) along the trendline. The price is consolidating above this trendline, suggesting a potential bounce back and a test of higher levels.
TF : 1W
Entry : $0.10200
Target : $0.18200
SL : $0.07670
#COTI/USDT analysis :
#COTI is in an uptrend, forming a structure of higher highs (HHs) and higher lows (HLs). The price has broken out and successfully retested the trendline, continuing its bullish trajectory. Currently, the price is trading above a minor resistance zone, which has now become a buy zone. It is anticipated that the price will rise further and test higher levels.
TF : 1H
Entry : $0.14450
Target : $0.16145
SL : $0.13292