#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
#COMP/USDT analysis :
#COMP is currently retracing towards the 200 EMA, creating a long trade opportunity. The price has swept the swing low stop losses and is now expected to resume bullish momentum, targeting higher levels.
TF : 4H
Entry : $56.50
Target : $61.52
SL : $53.93