#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
#RAD/USDT analysis :
#RAD is currently experiencing a bearish trend, trading below the 200 EMA. The price is forming lower lows (LLs) and lower highs (LHs).
At present, it is testing the resistance zone along with the 200 EMA. A reversal is anticipated from this level, allowing the price to resume its bearish momentum and potentially test lower levels.
TF : 1H
Entry : $0.840
Target : $0.779
SL : $0.875
#RAD/USDT analysis :
#RAD has broken out and retested the previous support levels. It is expected to reject from the current level and test lower levels.
TF : 1h
Entry : $1.209
Target : $1.127
SL : $1.268