#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
#CTSI/USDT analysis :
#CTSI is currently trading at a support zone and is expected to bounce back from this level, rising to test the resistance zone. A potential gain of 140% is anticipated from the current level.
TF : 1W
Entry : $0.1405
Target : $0.3404
SL : $0.0888
#CTSI/USDT analysis :
#CTSI has bounced off from the support zone and is expected to continue its bullish momentum towards the previous swing high.
TF : 30min
Entry : $0.1250
Target : $0.1295
SL : $0.1218