#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
🎯🎯 Non Stop Huge profit on Premium signals for all Premium Members
🔥🔥#TRU/USDT has crossed all the profit targets and made a great profit of 561%
👁🗨Contact @futurechief to enter the Premium Futures & SPOT Group for daily gains
#TRU/USDT analysis :
#TRU is retracing towards the 200 EMA, indicating a potential retest before continuing its bearish momentum. This level offers a good opportunity for a long entry.
TF : 2h
Entry : $0.0460
Target : $0.0520
SL : $0.0420
#TRU/USDT analysis :
#TRU is currently in a downtrend and is expected to maintain its bearish momentum, potentially testing lower price levels.
TF : 1H
Entry : $0.0713
Target : $0.0606
SL : $0.0782
✅✅ 55% Profit on #TRU/USDT for our Premium Members on On Binance Futures, Bitget Futures, ByBit USDT, KuCoin Futures, OKX Futures
👆🏻All Profit Targets Successfully Completed
👁🗨Contact @primemod to enter the most powerful premium group & make daily gains
#TRU +143% 🤑💸💰
CoinLegs' unique algorithms give you invaluable insights into the market.
Alerts, AI, Algotrade, Algorithms and much more at CoinLegs platform.
Now is the time to earn!
www.coinlegs.com