#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
#MASK/USDT analysis :
#MASK is currently in an uptrend, characterized by higher highs (HHs) and higher lows (HLs), and is trading above the 200-period exponential moving average (EMA).
The price is presently consolidating above a key support zone, and it is anticipated that it will bounce back from this level, continuing its upward trajectory and potentially testing previous highs.
TF : 4H
Entry : $2.877
Target : $3.300
SL : $2.610
#MASK/USDT analysis :
#MASK is in an uptrend. After breaking above the 200 EMA, the price is now sustaining above it. The price is currently consolidating over the support zone and is expected to bounce back from the current level, testing higher levels. Wait for a pullback before entering a long position.
TF : 4h
Entry : $2.265
Target : $2.531
SL : $2.129