#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
🚀🚀 Storming Profits in the Premium Group
🎯🎯#FLOW/USDT has covered all the targets to give an amazing profit of 36% for all Premium Members
👁🗨Contact @futurechief to enter the premium group & make daily gains on Futures as well as Spot Market
🎯🎯 Outstanding 451% Profit on #FLOW/USDT for all Premium Members
💹Here is the price chart of #FLOW/USDT - You can see the perfect Entry point of Premium members which resulted in huge profit - These are the benefits of being Premium member
👁🗨Contact @futurechief to enter the Premium Binance Futures/Bybit/Okex/Kucoin & SPOT Group for daily gain
#FLOW/USDT analysis :
#FLOW has broken out above the 200 EMA and has retraced back to it. The price is currently in a correction phase, consolidating sideways over the support zone. The price has rebounded and broken out of the trendline. Now, the price is expected to resume its bullish momentum and test higher levels.
TF : 1D
Entry : $0.798
Target : $1.271
SL : $0.620