#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
#CFX/USDT analysis :
#CFX is presently trading within a resistance zone from where the price is expected to reject and exhibit a bearish move. The price is currently in an impulsive phase, with a corrective pullback pending and expected to form from the current zone.
TF : 1h
Entry : $0.1852
Target : $0.1566
SL : $0.1982