#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
#BAKE/USDT analysis :
#BAKE is currently retracing towards the 200 EMA. The price is expected to retest this moving average before continuing its bearish momentum. The current level presents a favorable opportunity for a long entry.
TF : 2h
Entry : $0.1507
Target : $0.1700
SL : $0.1414
#BAKE/USDT analysis :
The price is in an uptrend, forming higher highs (HHs) and higher lows (HLs) above the 200-period exponential moving average (200 EMA). The price is expected to bounce back from this level and continue its bullish momentum, aiming to test the previous swing high.
TF : 1h
Entry : $0.2783
Target : $0.2929
SL : $0.2705