#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
#ATA/USDT analysis :
#ATA is currently finding support above the 200-period exponential moving average (200 EMA) within the support zone. The price is anticipated to test this zone and maintain its bullish momentum to reach the previous swing high.
TF : 4h
Entry : $0.0900
Target : $0.0983
SL : $0.0850
Currently, #ATAUSDT is compressed within a falling wedge pattern, a classic bullish reversal signal.
Should #ATA fail to bounce back from the $0.0820-$0.0700 support, our eyes will be on the next critical level at $0.0580. Historically, this level has been a stronghold, and the probability of a rebound here is notably higher.
But if $ATA breaks below these key support levels, the bears might take control, potentially leading to a bearish continuation.