#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
#CHZ/USDT analysis :
#CHZ is currently in a downtrend and making new lows. Look for a price retracement to test the resistance zone for a short entry opportunity. Lower levels will be the target level.
TF : 2H
Entry : $0.04862
Target : $0.04200
SL : $0.05193
#CHZ/USDT analysis :
#CHZ is in an uptrend, trading above the 200 EMA. After the breakout of the previous swing high, the price is sustaining above it. The price is anticipated to continue its bullish momentum and rise higher. Wait for a pullback to the support zone for a long entry.
TF : 2H
Entry : $0.0535
Target : $0.0560
SL : $0.0519